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Anthropic Economic Index, Microsoft’s ExACT, OpenThinker-32B, Huginn-3.5BYour Voice Matters – Help Improve Data Science Learning! (+ Get a Free eBook!)Help us enhance Data Science learning with your insights! Take a 5-8 min survey and get:✅ A free Packt eBook ($18 value)✅ Influence future books & courses✅ Early access to new features & perks!👉Take the Survey Now!📢 Welcome toDataPro #127~ Your Weekly Dose of Data Science & ML Innovation!The world of AI, machine learning, and data science never slows down, and we’re here to keep you on top of the latest innovations! From OpenAI’s o1 model for financial analysis to ArcticDB’s performance against Pandas, and Meta AI’s new CoCoMix framework, this edition is packed with breakthroughs, must-know tools, and career insights.🌟 Special Feature: An exclusive look at Python for Algorithmic Trading Cookbook by Jason Strimpel, helping you design, backtest, and deploy trading strategies with Python!🚀 In This Issue:🔹 Financial Analysis & AI: OpenAI’s o1 model is transforming financial research.🔹 Big Data Handling: Why ArcticDB is outperforming Pandas for massive datasets.🔹 AI Pretraining Advances: Meta AI introduces CoCoMix, improving LLM efficiency🔹 Scaling Vision-Language Models: Google DeepMind’s WebLI-100B dataset enhances cultural diversity.🔹 AI & Databases: Google’s Gen AI Toolbox for Databases streamlines AI-driven data access.💡 Also Inside:🔹 Microsoft’s ExACT framework for AI decision-making.🔹 Anthropic’s Economic Index: A deep dive into AI’s real-world economic role.🔹 OpenThinker-32B: A powerful open-data reasoning model.🔹 Huginn-3.5B: A new approach to scalable latent AI computation.🔹5 LLM Prompting Techniques that every developer should master.📩 Enjoy this issue & stay ahead in the Data & AI game!💬 Got a topic you’d love us to cover? Let us know!Cheers,Merlyn ShelleyGrowth Lead, Packt.✨ Featured Deep DiveOptimizing Financial Market Data with pandas: Analysis and TransformationHave you ever found yourself drowning in rows and columns of market data, wondering how to make sense of it all?You’re not alone.The good news is that pandas is here to help!Originally created by Wes McKinney at AQR Capital Management, pandas has grown into the go-to Python library for financial data analysis. Since its open-source release in 2009, it has empowered analysts to manipulate, transform, and analyze data with ease.If you work with financial data, whether for trading, risk management, or portfolio analysis, you need pandas in your toolkit. With its rich support for time series, data transformation, and handling missing values, pandas is the perfect solution for making sense of complex datasets. In this guide, we’ll walk through the essentials of pandas for financial market analysis. By the end, you’ll have the confidence to apply these tools in real-world scenarios.Getting Started with pandas Data StructuresBuilding Series and DataFramesThink of aSeriesas a column in Excel, but smarter. It’s a one-dimensional labeled array that can hold any data type. Let’s create a simple Series:import pandas as pd import numpy as np# Create a simple Series series = pd.Series([10, 20, 30, 40, 50], index=['A', 'B', 'C', 'D', 'E']) print(series)On the other hand, aDataFrameis a full table, imagine a spreadsheet where every column is a Series.# Creating a DataFrame data = {'Stock': ['AAPL', 'MSFT', 'GOOG'], 'Price': [150, 300, 2800], 'Volume': [10000, 15000, 12000]} df = pd.DataFrame(data) print(df)DataFrames make it easy to manipulate and analyze structured data, which is essential in financial market analysis.Handling Indexes in Financial DataUnderstanding pandas IndexingIndexes help you efficiently retrieve and align data. Different types include:Int64Index: Standard integer-based indexing.DatetimeIndex: Perfect for time series data.MultiIndex: Allows hierarchical indexing, great for financial datasets.Creating a DatetimeIndex for Financial Datadates = pd.date_range("2023–01–01", periods=10, freq='D') print(dates)MultiIndex for Market Datatuples = [('2023–07–10', 'AAPL'), ('2023–07–10', 'MSFT'), ('2023–07–10', 'GOOG')] multi_index = pd.MultiIndex.from_tuples(tuples, names=["date", "symbol"]) print(multi_index)Manipulating and Transforming Financial DataSelecting and Filtering DataEver needed to find just the right slice of market data? pandas makes it easy with .loc and .iloc:# Selecting by label print(df.loc[df['Stock'] == 'AAPL']) # Selecting by position print(df.iloc[0])Handling Missing DataMissing data is inevitable. pandas offers .fillna() and .dropna() to deal with it:df_missing = pd.DataFrame({'Stock': ['AAPL', 'MSFT', 'GOOG'], 'Price': [150, np.nan, 2800]}) print(df_missing.fillna(df_missing.mean()))Financial Market Analysis with pandasCalculating Asset ReturnsUnderstanding stock returns is crucial. You can compute daily returns using .pct_change():prices = pd.Series([100, 105, 103, 110, 120]) returns = prices.pct_change() print(returns)Measuring VolatilityVolatility reflects price fluctuations — critical for risk assessment:volatility = returns.std() print("Volatility:", volatility)Generating a Cumulative Return SeriesCumulative returns show total performance over time:cumulative_returns = (1 + returns).cumprod() print(cumulative_returns)Resampling and Aggregating Time Series DataFinancial analysts often need to adjust time frames, say, from daily to weekly data:date_rng = pd.date_range(start='2023–01–01', periods=30, freq='D') data = pd.DataFrame({'Date': date_rng, 'Price': np.random.randn(30) * 5 + 100}) data.set_index('Date', inplace=True) # Resample to weekly frequency weekly_data = data.resample('W').mean() print(weekly_data)Applying Custom Functions to Time Series DataWant a moving average? pandas makes it simple:def moving_average(series, window=3): return series.rolling(window=window).mean() # Apply a moving average function data['MA'] = moving_average(data['Price']) print(data)Conclusionpandas is an indispensable tool for financial data analysis. From handling missing data to calculating returns and volatility, it provides everything you need to extract meaningful insights from market data. Now that you’ve got the fundamentals down, you can confidently explore more advanced financial analytics, perhaps even integrating machine learning models for deeper insights.Elevate Your Algorithmic Trading GameThis article is based onPython for Algorithmic Trading CookbookbyJason Strimpel, a detailed guide to designing, building, and deploying algorithmic trading strategies using Python. Whether you’re a trader, investor, or Python developer, this book equips you with hands-on recipes to acquire, visualize, and analyze market data, design and backtest trading strategies, and deploy them in a live environment.If you’re ready to take your algorithmic trading skills to the next level, this book is your roadmap. Get your hands on a copy and start building smarter, more efficient trading strategies today!Get your copy here 👉Python for Algorithmic Trading Cookbook🔍 Fresh Insights from OpenAI ⋆✴︎˚｡⋆❯❯❯❯ Sharing the latest Model Spec: OpenAI updated its Model Spec, reinforcing customizability, transparency, and intellectual freedom while maintaining safety. Now open-sourced under CC0, it prioritizes user control, objectivity, and harm prevention. OpenAI will iteratively refine it, using real-world prompts and public feedback for improvements.❯❯❯❯ Shaping the future of retail with AI: This blog highlights Wayfair’s AI-driven transformation across ecommerce, operations, and employee training. CTO Fiona Tan discusses AI’s role in personalization, supply chain optimization, marketing, and modernizing legacy systems. Wayfair leverages ChatGPT and OpenAI APIs to enhance customer experience, automate tasks, and drive innovation.❯❯❯❯ Using AI to focus on the big picture: This blog explores how Fanatics Betting and Gaming integrates AI into finance and operations. CFO Andrea Ellis discusses AI-driven efficiency, including automating vendor identification, accelerating data analysis, and improving strategic decision-making. The company fosters broad AI adoption through structured training, task forces, and custom GPT development.❯❯❯❯ Using OpenAI o1 for financial analysis: This blog highlights how Rogo leverages OpenAI’s models to transform financial research and workflows for investment banks, private equity, and asset managers. By fine-tuning GPT-4o and o1 models, Rogo delivers real-time insights, automates diligence, and optimizes financial decision-making, saving analysts 10+ hours weekly.❯❯❯❯ Introducing the Intelligence Age: This blog explores how AI, like past groundbreaking innovations, is transforming human potential. OpenAI highlights ChatGPT’s rapid adoption, its role in education, science, medicine, and government, and its Super Bowl ad showcasing AI’s historical significance. The goal: ensuring AGI benefits everyone, driving progress and creativity.❯❯❯❯ OpenAI partners with Schibsted Media Group: This blog announces OpenAI’s partnership with Schibsted Media Group, integrating content from VG, Aftenposten, Aftonbladet, and Svenska Dagbladet into ChatGPT for up-to-date news summaries with attribution. The collaboration supports quality journalism, AI-driven content innovation, and new commercial opportunities in digital media.🚀 Trendspotting: What's Next in Tech Trends❯❯❯❯ Method of Moments Estimation with Python Code: This blog explains Method of Moments (MoM) estimation, a statistical technique for inferring probability distributions from data. Using Python code, it demonstrates MoM for Poisson and Normal distributions, showing how to estimate parameters efficiently and compare estimated distributions to true distributions for validation.❯❯❯❯ Pandas Can’t Handle This: How ArcticDB Powers Massive Datasets. This blog explores how ArcticDB outperforms Pandas for massive datasets, enabling faster data processing, better memory management, and scalability. It demonstrates ArcticDB’s use in finance and climate research, compares performance with Pandas, and highlights when to use ArcticDB for large-scale, production-ready analytics.❯❯❯❯ 7 Tools You Cannot Live Without as a Data Scientist: This blog highlights seven essential tools for data scientists, focusing on AI-driven and productivity-enhancing software. It covers Google Workspace for organization, You.com for research, Cursor for coding, and Grammarly for writing assistance, showcasing how these tools improve efficiency, workflow, and project management.❯❯❯❯ Microsoft Research and Physics Wallah team up to enhance AI-based tutoring: This blog explores Microsoft Research’s collaboration with Physics Wallah to enhance AI-powered tutoring in India using GPT-4o and advanced reasoning models. It highlights AI Guru, Smart Doubt Engine, and AI Grader, improving student accessibility, affordability, and learning outcomes for competitive exam preparation.❯❯❯❯ Meta AI Introduces CoCoMix: A Pretraining Framework Integrating Token Prediction with Continuous Concepts. This blog introduces Meta AI’s CoCoMix, an alternative LLM pretraining approach that integrates token prediction with concept-based reasoning using Sparse Autoencoders (SAEs). CoCoMix improves sample efficiency, generalization, knowledge transfer, and interpretability, offering a more structured and transparent model training paradigm.❯❯❯❯ Google DeepMind Research Introduces WebLI-100B: Scaling Vision-Language Pretraining to 100 Billion Examples for Cultural Diversity and Multilingualit. This blog discusses Google DeepMind’s WebLI-100B dataset, which scales vision-language model training to 100 billion image-text pairs. The dataset improves cultural diversity, multilinguality, and inclusivity, though traditional benchmarks show limited gains. It highlights bias challenges, dataset filtering trade-offs, and future research directions for more balanced AI models.🛠️ Platform Showdown: Comparing ML Tools & Services❯❯❯❯ Data Science Showdown: Which Tools Will Gain Ground in 2025: This blog forecasts emerging data science tools for 2025, highlighting PySpark, Numba, and Julia for big data computing, D3.js and Plotly for visualization, Streamlit, MLflow, and H2O.ai for model deployment, and OpenRefine for data preparation. It also discusses Google Cloud Platform’s growing prominence in AI and big data.❯❯❯❯ Building Multilingual Applications with Hugging Face Transformers: This blog explores building multilingual applications using Hugging Face Transformers, showcasing pre-trained models like XLM-R and mBERT for sentiment analysis, question answering, and summarization. It provides Python code, fine-tuning steps, and deployment tips to make multilingual AI integration easier and scalable.❯❯❯❯ Build a dynamic, role-based AI agent using Amazon Bedrock inline agents: This blog explores building dynamic AI assistants using Amazon Bedrock inline agents, enabling real-time tool selection, adaptive configurations, and role-based personalization. It showcases an HR assistant example, demonstrating flexibility in AI workflows, cost optimization, and scalability without managing multiple agent configurations.❯❯❯❯ Announcing Gen AI Toolbox for Databases: This blog announces the public beta of Gen AI Toolbox for Databases, an open-source server that connects agent-based generative AI applications to databases. Built in partnership with LangChain, it enhances scalability, security, observability, and tool management, supporting PostgreSQL, MySQL, and Google Cloud databases for seamless AI-driven data access.❯❯❯❯ Anthropic AI Launches the Anthropic Economic Index: A Data-Driven Look at AI's Economic Role. This blog introduces the Anthropic Economic Index, a data-driven initiative tracking AI’s economic impact across industries. Analyzing millions of anonymized Claude conversations, it maps AI adoption by occupation, highlighting high usage in software, writing, and analytical tasks while noting limited adoption in physical labor and specialized fields.❯❯❯❯ Meet Huginn-3.5B: A New AI Reasoning Model with Scalable Latent Computation. This blog introduces Huginn-3.5B, an AI model leveraging recurrent depth reasoning to dynamically refine computations in latent space. Unlike traditional Chain-of-Thought methods, it scales compute per task, improves efficiency without large context windows, and outperforms larger models in reasoning benchmarks.📊 Success Stories: Real-World ML Case Studies❯❯❯❯ 5 LLM Prompting Techniques Every Developer Should Know: This blog explores five essential LLM prompting techniques for developers: zero-shot, few-shot, chain-of-thought, tree-of-thought, and self-consistency prompting. These methods improve AI accuracy, reasoning, and response consistency, helping developers optimize interactions with models like Claude AI and ChatGPT for better results.❯❯❯❯ 10 Little-Known Python Libraries That Will Make You Feel Like a Data Wizard: This blog introduces 10 lesser-known Python libraries that enhance data science workflows, covering visualization (Altair), SQL analytics (DuckDB), geospatial analysis (H3), automated profiling (Ydata Profiling), dependency management (Poetry), graph analysis (NetworkX), scalable ML (H2O.ai, PyCaret), and missing data visualization (Missingno).❯❯❯❯ Should Data Scientists Care About Quantum Computing? This blog explores the relevance of quantum computing for data scientists, addressing how AI accelerates quantum advancements and how quantum computing could optimize data science workflows in the future. While not yet essential for most data scientists, being quantum-aware offers a strategic advantage as the field evolves.❯❯❯❯ ExACT: Improving AI agents’ decision-making via test-time compute scaling. This blog introduces Microsoft’s ExACT, a new AI framework that enhances autonomous agents' decision-making through Reflective-MCTS (R-MCTS) and Exploratory Learning. ExACT improves reasoning, adaptability, and generalization in dynamic environments, achieving state-of-the-art performance in AI benchmarks like VisualWebArena and OSWorld.❯❯❯❯ 3 Ways to Secure Your Data Science Job From Layoffs in 2025: This blog outlines three key strategies to secure your data science job amid growing AI-driven layoffs in 2025. It emphasizes the importance of strong foundational skills, business-facing roles, and workplace visibility to stay competitive and thrive in an evolving job market.❯❯❯❯ Meet OpenThinker-32B: A State-of-the-Art Open-Data Reasoning Model. This blog introduces OpenThinker-32B, an open-data reasoning model designed to excel in mathematics, coding, and scientific inquiry. Fine-tuned from Qwen2.5-32B-Instruct and trained on the OpenThoughts-114k dataset, it achieves state-of-the-art performance across reasoning benchmarks, offering an open-source alternative for AI research and development.We’ve got more great things coming your way, see you soon!*{box-sizing:border-box}body{margin:0;padding:0}a[x-apple-data-detectors]{color:inherit!important;text-decoration:inherit!important}#MessageViewBody a{color:inherit;text-decoration:none}p{line-height:inherit}.desktop_hide,.desktop_hide table{mso-hide:all;display:none;max-height:0;overflow:hidden}.image_block img+div{display:none}sub,sup{font-size:75%;line-height:0} @media (max-width: 100%;display:block}.mobile_hide{min-height:0;max-height:0;max-width: 100%;overflow:hidden;font-size:0}.desktop_hide,.desktop_hide table{display:table!important;max-height:none!important}}

Factory’s AI-powered Dev Platform, OpenAI’s new API tools, Python’s Asyncio Library👋 Hello ,📢 Welcome toDataPro #130~ Your Weekly Dose of Data Science & ML Innovation!AI is moving fast, but are your workflows keeping up?Every day, data professionals are tasked with building smarter AI systems, managing massive datasets, and optimizing workflows, all while staying ahead of the latest breakthroughs. The data-driven world isn’t slowing down, and neither should you.This week, we’re diving into next-gen AI automation, powerful ML tools, and real-world case studies that will level up your data science game.🔍 Here’s what’s inside:💡 AI-powered automation: We compare Manus AI vs. DeepSeek R1 to see which model is redefining task automation for data analysts, engineers, and ML teams.⚡ Smarter, faster queries: Learn how ScaNN for AlloyDB outperforms pgvector HNSW in scalable vector search, making AI search, fraud detection, and recommendations lightning-fast.🤖 Multi-agent AI systems on AWS: The future of AI isn’t just about one model, it’s about many models working together. We break down how AI agents collaborate to streamline decision-making.🧠 Teaching AI to reason, not just predict: Logic-RL is a game-changer for AI’s problem-solving capabilities. Can AI truly think before it speaks?💻 AI-driven software engineering: Factory’s AI-powered dev platform is cutting engineering cycles by 20% with OpenAI’s reasoning models, is this the next step toward autonomous coding?🌟 Emerging Trends: What’s Next?🔹 Google’s Gemma 3 brings multimodal, on-device AI to the masses.🔹 Hugging Face’s OlympicCoder is solving olympiad-level programming challenges ~ can AI outperform human coders?🔹 Microsoft’s Semantic Telemetry is redefining how we analyze AI-user interactions in Copilot and Bing.🔹 Alibaba’s R1-Omni is pushing the boundaries of multimodal AI and emotion recognition.⚒️ Tool Showdowns & Hands-on Guides:🔹 DBeaver’s hidden SQL tricks ~ 7 expert tips to optimize your queries.🔹 Switching from Data Analyst to Data Scientist? This guide breaks it down step-by-step.🔹 Mastering Apache Airflow ~ A modern guide to scalable workflow automation.🎯 Real-world success stories:📌 LY Corporation & OpenAI ~ AI-powered content generation, search, and user engagement at scale.📌 OpenAI’s new API tools ~ Are you ready for multi-agent AI applications?💡 Bottom line? AI is evolving. Whether you’re a data scientist, ML engineer, or AI enthusiast, staying ahead means adopting new tools, refining your skills, and embracing automation.⚡ Read on, experiment, and innovate. The future of data science is being built right now ~ are you in?🔗 Dive into this week’s top stories below!Cheers,Merlyn ShelleyGrowth Lead, Packt📚 Limited-Time Offer: 30% Off Bestselling eBooks!Data Governance HandbookBy Wendy S. BatchelderWith 2.5 quintillion bytes of data generated daily, effective data governance is more crucial than ever. The Data Governance Handbook equips data professionals with practical strategies to ensure trustworthy, business-aligned data solutions.No coding or sales expertise needed, just a clear, results-driven approach to mastering data governance. Ready to transform your data strategy? This book is for you.Buy eBook $39.99 $27.98Learn Microsoft FabricBy Arshad Ali, SchachtMicrosoft Fabric is the ultimate unified analytics solution for the AI era, seamlessly integrating data engineering, real-time analytics, AI, and visualization in one platform.No matter your data role, this book provides a practical, hands-on guide to mastering Microsoft Fabric. Future-proof your data analytics journey today!Buy eBook$35.99 $24.99Microsoft Power BI CookbookBy Greg Deckler, PowellThe Power BI Cookbook is the go-to resource for BI professionals and data analysts looking to master data integration, visualization, and advanced reporting in Power BI. This updated edition brings the latest Microsoft Data Fabric capabilities, Hybrid tables, and AI-driven enhancements, helping you build powerful, future-ready BI solutions.Packed with step-by-step guidance and real-world use cases, this book ensures you stay ahead in the evolving Power BI landscape. Take your Power BI expertise to the next level!Buy eBook $43.99 $29.99Artificial Intelligence for CybersecurityBy Bojan Kolosnjaji, Huang Xiao, Peng Xu, Apostolis ZarrasArtificial Intelligence is transforming cybersecurity, enabling faster threat detection, smarter authentication, and more resilient defenses. This book bridges the gap between AI and cybersecurity, providing practical guidance, step-by-step exercises, and real-world applications to help professionals design, implement, and evaluate AI-driven security solutions.Packed with practical insights and expert guidance, this book ensures you can confidently integrate AI into your cybersecurity strategy. Stay ahead of cyber threats with AI-powered defense strategies!Buy eBook $35.99$24.99Hands-On Machine Learning with C++By Kirill KolodiazhnyiHarness the power of machine learning and deep learning using C++ with this hands-on guide. Written by an experienced software engineer, this book walks you through data processing, model selection, and performance optimization, equipping you with the skills to build and deploy efficient ML models on mobile and embedded devices.With practical examples, real-world use cases, and step-by-step guidance, this book ensures you can apply ML techniques effectively in C++. Master ML with C++ and take your models to production!Buy eBook $39.99 $27.98Python for Algorithmic Trading CookbookBy Jason StrimpelWant to build, test, and deploy algorithmic trading strategies like a pro? This book is your hands-on guide to turning Python into a powerful trading engine. Whether you're a retail trader, quant investor, or Python developer, this book equips you with practical, ready-to-use code to design, test, and deploy trading strategies with confidence.📖 Get your copy & start building smarter trading algorithms today!Buy eBook $47.99$32.99🔍 Fresh Insights ⋆✴︎˚｡⋆Manus AI vs. DeepSeek R1: Redefining AI-Powered Task Automation for Data ProfessionalsThis blog compares Manus AI and DeepSeek R1, two advanced AI models designed for task automation and workflow management. It evaluates their capabilities in data analysis, coding, content automation, and AI-driven productivity, highlighting Manus AI's autonomy vs. DeepSeek R1's text-generation strengths.Scalable Vector Search with ScaNN for AlloyDBThis blog explores ScaNN for AlloyDB, a breakthrough in scalable vector search for large datasets. It compares ScaNN vs. pgvector HNSW, highlighting faster queries, lower memory use, and cost-efficient indexing for AI search, fraud detection, and recommendation systems in PostgreSQL environments.AI That Works in Teams: Multi-Agent Systems on AWSThis blog explores multi-agent AI systems using LangGraph and Mistral on AWS, highlighting their collaborative approach to AI-driven automation. It discusses workflow orchestration, real-world applications, and benefits for data professionals, showcasing how AI agents can optimize decision-making and streamline complex tasks.Logic-RL: The AI Breakthrough That Teaches Machines to Think This blog explores Logic-RL, a reinforcement learning method that trains AI to think step by step rather than just predict answers. It highlights structured reasoning, improved problem-solving, and real-world applications in education, law, finance, and AI assistants, redefining how AI approaches logical challenges.Accelerating engineering cycles 20% with OpenAI This blog explores Factory's AI-powered development platform, which integrates OpenAI's reasoning models (o1, o3-mini, GPT-4o) to accelerate software development. It highlights faster coding cycles, automated knowledge retrieval, and AI-driven planning, positioning Factory as a step toward autonomous software engineering.Protect Data Privacy and Optimize AI Models with Tonic TextualLLMs have tapped all of pubically available data. The last mile training of models requires private data. Use private data without compromising security. Redact, label, and prep freetext for LLM ingestion or data pipelines.Start Free TrialSponsored🚀 Trendspotting: What's Next in Tech TrendsGoogle AI Releases Gemma 3: Lightweight Multimodal Open Models for Efficient and On‑Device AIThis blog introduces Gemma 3, Google DeepMind’s latest lightweight, multimodal AI models designed for efficient on-device performance. It highlights portability, multilingual support, expanded context windows, and hardware compatibility, making advanced AI more accessible to developers without compromising performance or safety.Hugging Face Releases OlympicCoder: A Series of Open Reasoning AI Models that can Solve Olympiad-Level Programming ProblemsThis blog introduces OlympicCoder, Hugging Face’s open-source reasoning AI models designed for olympiad-level programming challenges. It highlights chain-of-thought training, outperforming closed-source models, and advanced problem-solving capabilities, making it a breakthrough in competitive programming AI.Semantic Telemetry: Understanding how users interact with AI systemsThis blog explores Semantic Telemetry, a Microsoft Research project designed to analyze how users interact with AI systems like Copilot in Bing. It introduces a new data science approach using LLMs to classify topics, task complexity, and behavioral insights, highlighting how AI chat differs from traditional search.Alibaba Researchers Introduce R1-Omni: An Application of Reinforcement Learning with Verifiable Reward (RLVR) to an Omni-Multimodal Large Language ModelAlibaba’s latest innovation, R1-Omni, applies Reinforcement Learning with Verifiable Reward (RLVR) to multimodal emotion recognition. By integrating visual and audio cues, it enhances accuracy, interpretability, and reasoning, setting a new standard for AI-driven emotional analysis.Salesforce AI Releases Text2Data: A Training Framework for Low-Resource Data GenerationText2Data, Salesforce AI’s latest training framework, enhances text-to-data generation in low-resource scenarios. By combining diffusion-based learning with constraint optimization, it improves controllability, prevents catastrophic forgetting, and maintains data distribution quality, making it a breakthrough for AI-driven data synthesis across multiple domains.🛠️ Platform Showdown: Comparing ML Tools & Services7 Powerful DBeaver Tips and Tricks to Improve Your SQL WorkflowDBeaver is a powerful open-source SQL IDE, and mastering its hidden features can significantly improve SQL workflows. This blog shares seven essential tips, including command palette navigation, SQL templates, column statistics, advanced copy options, and custom formatters, helping users streamline database querying and data analysis.How to Switch from Data Analyst to Data ScientistSwitching from Data Analyst to Data Scientist requires the right skills, strategy, and preparation. This blog explores key technical skills, learning resources, portfolio building, and job-hunting strategies, helping analysts transition into machine learning, AI, and predictive modeling roles while leveraging their existing expertise.Heatmaps for Time SeriesHeatmaps for Time Series provide a powerful way to visualize trends, outliers, and temporal patterns in data. This blog explores how to create effective heatmaps with Python’s Matplotlib, emphasizing color choices, normalization, and handling missing data, making complex datasets easier to interpret and analyze.Custom Training Pipeline for Object Detection ModelsCustom Training Pipeline for Object Detection explores building a fully customizable object detection pipeline from scratch. This blog covers dataset processing, augmentations, training strategies, and evaluation metrics, comparing D-FINE and YOLO models to optimize accuracy, speed, and efficiency for real-world detection tasks.Your Salesforce Data, Your Responsibility: Best Practices for Data ProtectionSponsored📊 Success Stories: Real-World ML Case StudiesGetting Started with Python’s asyncio LibraryPython’s asyncio library enables asynchronous programming for handling multiple tasks concurrently without blocking execution. This guide explores event loops, coroutines, tasks, and futures, demonstrating how to use async/await, asyncio.gather(), and asyncio.wait_for() to optimize performance in network requests and I/O operations.A Practical Guide to Modern Airflow Apache Airflow has become a critical tool for workflow orchestration, helping data engineers and machine learning professionals manage complex pipelines efficiently. This guide explores DAGs, operators, scheduling, and XComs, offering a practical approach to installing, configuring, and optimizing Airflow for scalable automation.Driving growth and ‘WOW’ moments with OpenAILY Corporation, one of Japan’s largest tech companies, is leveraging OpenAI’s API to enhance its platforms, including LINE and Yahoo! JAPAN. This collaboration focuses on AI-driven search, productivity tools, and content generation, improving user experiences, operational efficiency, and revenue growth while ensuring data security and ethical AI adoption.New tools for building agents - OpenAIOpenAI has introduced new tools and APIs to help developers build advanced AI agents. The Responses API now combines chat and tool-use capabilities, making it easier to integrate web search, file search, and computer use directly into AI workflows. Alongside the new Agents SDK and observability tools, these features streamline multi-agent orchestration and workflow execution. OpenAI also plans to deprecate the Assistants API by mid-2026 in favor of this new approach, ensuring more flexible, scalable, and efficient agent development.We’ve got more great things coming your way, see you soon!🔍Stay Ahead in Data Science! 📊If you are new here, subscribe to DataPro, Packt’s newsletter for the latest data insights, trends, and expert analysis, and get a FREE eBook to kickstart your learning!📩 Join now & claim your free eBook! 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Google DeepMind’s CaMeL, Dr. GRPO by Sea AI Lab, SpatialLM-Llama-1BHow to Balance Cloud Agility, Cost, and RiskJoin cybersecurity thought leader David Linthicum for a special fireside chat to learn how to use AI and ML to unify your data strategies, uncover hidden cloud costs, and overcome the limitations of your traditional data protection in public cloud environments.Save Your SpotSponsoredSubscribe | Submit a tip | Advertise with us📡 DataPro Newsletter 132: Solving Real-World AI & Data ChallengesThis week, we spotlight innovative tools, research, and insights that help data professionals tackle complex problems with ease.🚀 Smarter AI, Better PerformanceStruggling with complex AI tasks? DeepSeek-V3-0324 boosts reasoning and code execution, while ByteDance’s InfiniteYou improves identity-preserved image generation. SpatialLM-Llama-1B enhances 3D scene understanding for robotics and navigation, and Orpheus 3B offers human-like speech synthesis with empathetic intonation and real-time low-latency streaming.🔥 Securing AI ModelsWorried about AI vulnerabilities? Google DeepMind’s CaMeL introduces a robust security layer that protects against prompt injection attacks without altering underlying models. Similarly, Dr. GRPO prevents response-length biases in LLMs, ensuring more accurate and fair AI outputs.💡 Scaling AI with EaseHigh compute costs holding you back? Anyscale on Google Cloud enables scalable AI workloads by optimizing GPU usage, lowering costs, and ensuring reliable AI scaling. Nuro’s transition to AlloyDB for PostgreSQL accelerates AI model training by improving query performance and reducing operational costs.🤖 Automate Supply Chain WorkflowsTired of manual processes slowing you down? n8n makes it easy to automate supply chain analytics workflows using AI-powered agents. From parsing emails to generating SQL queries and updating databases, this low-code platform empowers non-technical teams to enhance workflow efficiency.📊 Reliable ML PredictionsNeed confidence in model predictions? ML Uncertainty provides an easy-to-use Python package that quantifies prediction reliability, enabling better decision-making by estimating uncertainties in ML models with minimal effort.🧠 Easy AI/ML Roadmap for BeginnersFeeling lost in the AI/ML space? Our Ultimate AI/ML Roadmap simplifies the learning path by covering essential math concepts, Python basics, data structures, and algorithms, giving aspiring professionals a strong foundation to apply AI/ML in real-world scenarios.🎨 Explore Neural Chaos & OptimizationCurious about neural dynamics and model optimization? Attractors in Neural Networks explores how feedback loops and nonlinear activations generate intricate, chaotic behaviors, while Least Squares explains why this classic regression method remains optimal, minimizing MSE and offering unbiased, accurate estimates.Plus 📚 Get 30% OFF Top Data Science Ebooks!Enhance your skills and stay ahead with 30% off on selected AI/ML and Data Science ebooks for a limited time.Keep scrolling for the full scoop!Cheers,Merlyn ShelleyGrowth Lead, Packt📚 Limited-Time Offer: 30% Off Bestselling eBooks!Buy NowBuy NowBuy NowBuy NowTop Tools Driving New Research 🔧📊⭕ deepseek-ai/DeepSeek-V3-0324: DeepSeek introduced V3-0324 with enhanced reasoning (MMLU-Pro +5.3, GPQA +9.3, AIME +19.8), better code execution, improved Chinese writing, refined translation, more accurate function calling, and detailed search analysis. New system prompt and optimized temperature mapping included.⭕ ByteDance/InfiniteYou: ByteDance introduced InfiniteYou (InfU), leveraging Diffusion Transformers (DiTs) like FLUX for high-fidelity, identity-preserved image generation. InfU improves identity similarity, text-image alignment, and aesthetics using InfuseNet and multi-stage training. Two model variants, aes_stage2 (better aesthetics) and sim_stage1 (higher ID similarity), enhance flexibility.⭕ manycore-research/SpatialLM-Llama-1B: SpatialLM introduced SpatialLM-Llama-1B, a 3D large language model that processes point cloud data to generate structured 3D scene understanding. It identifies architectural elements (walls, doors, windows) and object bounding boxes. It supports multimodal inputs, enhancing applications in robotics and navigation.⭕ canopylabs/orpheus-3b-0.1-ft: Canopy Labs introduced Orpheus 3B 0.1 FT, a Llama-based speech model fine-tuned for high-quality, empathetic text-to-speech generation. It offers human-like intonation, zero-shot voice cloning, guided emotions, and low-latency real-time streaming, making it ideal for natural speech synthesis applications.⭕19 Git Tips For Everyday Use: The post shares practical Git commands and techniques to improve workflow efficiency. It covers logging, file extraction, rebasing, managing branches, fixing commits, using aliases, and troubleshooting, offering valuable insights for intermediate Git users.⭕ AI Expert Roadmap: This post offers an interactive collection of roadmaps covering AI, data science, machine learning, deep learning, and big data engineering. It guides learners on essential concepts, tools, and techniques while encouraging ongoing exploration of evolving technologies and best practices.⭕ Cookiecutter Data Science: The Cookiecutter Data Science v2 introduces an improved, standardized project structure for data science workflows. It offers a command-line tool (ccds) that simplifies project setup and enforces best practices. With enhanced functionality and flexible directory organization, it ensures consistency and reproducibility across projects.📚 Limited-Time Offer: 30% Off Bestselling eBooks!Buy NowBuy NowBuy NowBuy NowTopics Catching Fire in Data Circles 🔥💬⭕ Google DeepMind Researchers Propose CaMeL: A Robust Defense that Creates a Protective System Layer around the LLM, Securing It even when Underlying Models may be Susceptible to Attacks. Google DeepMind introduces CaMeL, a security layer that protects LLMs from prompt injection attacks without modifying the underlying models. Using a dual-model architecture and metadata-based policies, CaMeL isolates untrusted data, ensuring safer decision-making and outperforming existing defenses in security and reliability.⭕ A Code Implementation for Advanced Human Pose Estimation Using MediaPipe, OpenCV and Matplotlib: This tutorial demonstrates advanced human pose estimation using MediaPipe, OpenCV, and Matplotlib. It guides developers through detecting, visualizing, and extracting keypoints from images, enabling applications in sports, healthcare, and interactive systems. The code efficiently processes and annotates pose landmarks with high accuracy.⭕ Sea AI Lab Researchers Introduce Dr. GRPO: A Bias-Free Reinforcement Learning Method that Enhances Math Reasoning Accuracy in Large Language Models Without Inflating Responses: Sea AI Lab introduces Dr. GRPO, a bias-free reinforcement learning method that improves LLMs’ math reasoning accuracy without inflating responses. It eliminates response-length biases, ensuring fair model updates. Dr. GRPO-trained models outperformed others on key benchmarks while maintaining efficiency and reducing unnecessary verbosity.New Case Studies from the Tech Titans 🚀💡⭕ Anyscale powers AI compute for any workload using Google Compute Engine: Anyscale, built on Google Compute Engine (GCE) and Kubernetes Engine (GKE), powers scalable AI workloads across diverse environments. By optimizing compute flexibility and performance, it enables efficient model training, inference, and deployment. Anyscale reduces costs, boosts GPU utilization, and ensures reliable AI scaling across industries.⭕ Formula E’s AI equation: A new Driver Agent for the next generation of racers. Formula E partners with Google Cloud to introduce the AI-powered Driver Agent, leveraging Vertex AI and Gemini to analyze multimodal racing data. This tool democratizes access to data-led coaching, helping aspiring drivers refine performance by comparing their laps with professional benchmarks.⭕ Nuro drives autonomous innovation with AlloyDB for PostgreSQL: Nuro enhances autonomous vehicle innovation by migrating to AlloyDB for PostgreSQL, enabling seamless data management, high query performance, and vector similarity searches. This transition reduces operational costs, accelerates AI model training, and ensures continuous improvement of autonomous driving systems across complex real-world scenarios.⭕ Enhance deployment guardrails with inference component rolling updates for Amazon SageMaker AI inference: Amazon SageMaker AI introduces rolling updates for inference components, enhancing model deployment by reducing resource overhead, preventing downtime, and enabling batch-based updates with automatic rollback safeguards. This feature optimizes resource use and ensures reliable, cost-effective updates for GPU-heavy workloads, maintaining high availability in production environments.⭕ Integrate natural language processing and generative AI with relational databases: Amazon introduces a solution integrating natural language processing (NLP) and generative AI using Amazon Bedrock and Aurora PostgreSQL. It enables users to query relational databases using conversational language, reducing SQL complexity, democratizing data access, and easing the burden on developers through AI-driven SQL generation.Blog Pulse: What’s Moving Minds 🧠✨⭕ Automate Supply Chain Analytics Workflows with AI Agents usingn8n: n8n revolutionizes supply chain analytics by enabling AI-powered workflow automation without extensive coding. Using pre-built nodes, users can build AI agents to process emails, generate SQL queries, and update databases. This low-code platform empowers non-technical teams to maintain and enhance workflows efficiently.⭕ Uncertainty Quantification in Machine Learning with an Easy Python Interface: ML Uncertainty is a Python package that simplifies uncertainty quantification (UQ) for machine learning models, providing reliable prediction intervals with minimal code. Built on top of SciPy and scikit-learn, it enables users to estimate uncertainties efficiently, enhancing model interpretability and real-world decision-making.⭕ The Ultimate AI/ML Roadmap for Beginners: This post guides aspiring professionals through the essential steps to master AI and machine learning. Covering math fundamentals, Python, data structures, and algorithms, this roadmap equips learners to apply AI/ML in real-world scenarios without requiring a PhD.⭕ Attractors in Neural Network Circuits:Beauty and Chaos. This article explores how neural networks, when modeled as dynamical systems, evolve over time and converge to attractors, fixed points, limit cycles, or chaotic patterns. By adding feedback loops and nonlinear activations, even simple neural networks generate intricate behaviors, offering insights into memory formation, oscillating reactions, and chaotic processes.⭕ Least Squares: Where Convenience Meets Optimality. Least Squares is the cornerstone of regression models, primarily because of its simplicity, mathematical optimality, and deep connection with Maximum Likelihood Estimation (MLE). Beyond its computational ease, it minimizes Mean Squared Error (MSE) efficiently, derives the mean as a natural consequence of L2 minimization, and provides the Best Linear Unbiased Estimator (BLUE) when applied to Ordinary Least-Squares (OLS).Buy NowBuy Now*{box-sizing:border-box}body{margin:0;padding:0}a[x-apple-data-detectors]{color:inherit!important;text-decoration:inherit!important}#MessageViewBody a{color:inherit;text-decoration:none}p{line-height:inherit}.desktop_hide,.desktop_hide table{mso-hide:all;display:none;max-height:0;overflow:hidden}.image_block img+div{display:none}sub,sup{font-size:75%;line-height:0} @media (max-width: 100%;display:block}.mobile_hide{min-height:0;max-height:0;max-width: 100%;overflow:hidden;font-size:0}.desktop_hide,.desktop_hide table{display:table!important;max-height:none!important}}

Verdi by Mercado Libre, Google FRAMES, NotebookLM, Vertex AI Prompt Optimizer, Logic-of-ThoughtIf you are not an AI-powered professional in 2024, you will either:--Get replaced by a person who uses AI--Face a slow career growth & lower salary--Keep spending 10s of hours on tasks that can be done in 10 minutes.But don’t fret– there is one resource that can CHANGE your life, but only if you’re ready to take action NOW.Best thing? It's usually $399, but it's absolutely free for the first 100 readers.Save your seat now (Offer valid for 24 hours only)Register here (first 100 people get it for free + $500 bonus) 🎁SponsoredWelcome to DataPro #114 – Your Weekly Data Science & MLWizardry!🌟Stay ahead in the fast-paced world of AI and ML with the latest insights, strategies, and game-changing tools. This week, we’re bringing you top picks fromtrending data resources to supercharge your projects, boost accuracy, and optimize performance. Ready to level up? Let’s dive in!🔍 Algorithm Spotlight: This Week’s Standout Models✦ MaskLLM: Streamlining LLM Sparsity Training for Big Datasets✦ Prithvi WxC: IBM & NASA’s 2.3B Parameter Model for Weather & Climate✦ LightLLM: High-Speed Python Framework for LLM Inference✦ CopilotKit CoAgents: Simplifying Human-AI Collaboration✦ Blockwise Parallel Decoding (BCD): KAIST & Google’s AI Breakthrough for Faster Language Models🚀 Tech Trends on the Rise✦ Efficient Knowledge Management: How Notion Powers Data Teams✦ Llama 3.2 Locally: Your Quick Start Guide✦ Data Formulator: AI-Powered Visualizations for Analysts✦ RadEdit: Stress-Test Biomedical Vision Models with Synthetic Data✦ OpenAI's Realtime API: Speed Meets Smarts✦ Verdi by Mercado Libre: AI Development Platform Powered by GPT-4o🛠️ Platform Showdown: Must-Try ML Tools & Services✦ Moving Averages with NumPy: Quick How-To✦ Llamafactory Setup: Installation Made Easy✦ ChatGPT for Translation: Bridging Language Gaps in Minnesota✦ Reinforcement Learning: Optimizing Inventory Management with Python✦ AI Agents: Rethinking Autonomy✦ Conversational AI: Solving the Data Democratization Puzzle📊 Real-World Wins: ML Success Stories✦ MALPOLON: AI for Species Distribution Modeling with Deep Learning✦ AMD-135M: AMD's First LLM Series Trained with 670B Tokens✦ MassiveDS: A 1.4 Trillion-Token Datastore for NLP Excellence✦ Vertex AI Prompt Optimizer: Boost Your Generative AI Solutions🌍 ML Newsflash: Industry Breakthroughs & Discoveries✦ Ovis-1.6: Aligning Visual and Textual Embeddings✦ Logic-of-Thought: Enhancing Reasoning in LLMs✦ Instructive Decoding (ID): Boosting Focus in Instruction-Tuned LLMs✦ NotebookLM: Now with Audio & YouTube Integration✦ Google FRAMES: New Dataset for Testing RAG ApplicationsThat’s all for this week’s data-driven insights!Last Chance! For the next 48 hours only, save $150 on your full event pass!BOOK NOW AT $399.99 $239.99Use code LASTCHANCE40 at checkoutImagine being part of 10+ Power Talks, 12+ Hands-On Workshops, and 3 Interactive Roundtables—while networking with 30+ top industry leaders and hundreds of tech professionals from across the globe. This is your opportunity to dive into cutting-edge AI solutions at the Generative AI in Action 2024 Conference.It’s all happening November 11-13 (Virtual)—don’t miss your chance!BOOK YOUR SEAT NOW before prices increase on Saturday!Take our weekly survey and get a free PDF copy of our best-selling book,"Interactive Data Visualization with Python - Second Edition."We appreciate your input and hope you enjoy the book!Share Your Insights and Shine! 🌟💬Cheers,Merlyn Shelley,Editor-in-Chief, Packt.📚 Packt Signature Series: Must-Reads & Author Insights➽ AI-Assisted Programming for Web and Machine Learning: Unlock the power of AI-assisted programming to streamline web development and machine learning. Learn to enhance frontend and backend coding, optimize ML models, and automate tasks using GitHub Copilot and ChatGPT. Perfect for boosting productivity and refining workflows. Start your free trial for access, renewing at $19.99/month.eBook $18.99 $38.99Print + eBook $32.99 $47.99➽ Machine Learning and Generative AI for Marketing: Leverage AI and Python to revolutionize your marketing strategies with predictive analytics and personalized content creation. Learn to combine advanced segmentation techniques and generative AI to boost customer engagement while ensuring ethical AI practices. Perfect for driving real business growth. Start your free trial for access, renewing at $19.99/month.eBook $19.99 $39.99Print + eBook $34.98 $49.99➽ Amazon DynamoDB - The Definitive Guide: Master Amazon DynamoDB with this comprehensive guide, learning key-value data modeling, optimized strategies for transitioning from RDBMS, and efficient read consistency. Discover advanced techniques like caching and analytics integration with AWS services to boost performance, while minimizing latency and costs. Start your free trial for access, renewing at $19.99/month.eBook $17.99 $35.99Print + eBook $30.99 $44.99🔍 Model Breakdown: Unveiling the Algorithm of the Week➽ MaskLLM: A Learnable AI for End-to-End Training of LLM Sparsity on Large Datasets. MaskLLM introduces a learnable pruning method for LLMs using N: M sparsity, reducing computational costs. Through Gumbel Softmax sampling, it enables end-to-end training on large datasets, outperforming existing methods like SparseGPT in perplexity and efficiency.➽ IBM and NASA Release Prithvi WxC: A 2.3B Parameter Foundation Model for Weather and Climate. Prithvi WxC, a 2.3 billion parameter model, uses transformer-based architecture for weather and climate forecasting. It efficiently captures global and local dependencies, outperforming existing models in predicting extreme events and reducing computational costs while generalizing across various forecasting tasks.➽ LightLLM: A Lightweight, Scalable, High-Speed Python Framework for LLM Inference and Serving. LightLLM is an efficient framework designed to deploy large language models (LLMs) in resource-constrained environments like mobile and edge devices. Using techniques such as quantization, pruning, and distillation, it reduces computational demands while maintaining accuracy, enhancing LLM accessibility and usability.➽ CopilotKit’s CoAgents: Simplifying Human Integration with LangGraph Agents. CopilotKit is an open-source framework enabling developers to build AI copilots and in-app agents with real-time context awareness. Its CoAgents beta release supports human-in-the-loop AI, enhancing collaboration between AI and human operators.➽ KAIST and Google AI Introduce Blockwise Parallel Decoding (BCD) to Enhance Efficiency and Fluency in Language Models. This blog discusses Blockwise Parallel Decoding (BPD), a method developed to speed up autoregressive language models by predicting multiple tokens simultaneously, reducing inference latency, and improving efficiency in natural language processing tasks like text generation.🚀 Trendspotting: What's Next in Tech Trends➽ Efficient Knowledge Management for Data Teams Using Notion: This blog explains how data teams can streamline knowledge management using Notion, a platform for productivity and collaboration, to consolidate scattered resources, manage tasks, and enhance team communication across projects efficiently.➽ Using Llama 3.2 Locally: This blog provides a tutorial on using the Msty application to access Llama 3.2 models locally and remotely. It covers downloading, installing, and utilizing lightweight and vision variants for multilingual text generation and image reasoning.➽ Data Formulator: Exploring how AI can help analysts create rich data visualizations: This blog introduces Data Formulator, an open-source tool combining AI and user interface interactions to create rich data visualizations. It enables iterative chart design, using natural language input and data threads for flexible, efficient data visualization.➽ Stress-testing biomedical vision models with RadEdit: A synthetic data approach for robust model deployment: This blog introduces RadEdit, a tool for stress-testing biomedical vision models by simulating dataset shifts using diffusion image editing. It helps researchers identify model weaknesses, ensuring reliable performance across diverse medical conditions and environments.➽ OpenAI’s Realtime API: This blog introduces the Realtime API, enabling developers to build low-latency, speech-to-speech experiences using GPT-4o. It simplifies conversational app development by handling natural voice interactions with a single API call.➽ Building agent + human collaboration with GPT-4o: Dr. Robert Yang founded Altera, a research lab creating "digital humans" capable of interacting and collaborating with people. Using GPT-4, Altera’s AI agents address data degradation, enabling long-term autonomy and emotional intelligence in virtual environments like Minecraft.➽ Mercado Libre Launches Verdi: AI Developer Platform Powered by GPT-4o. This blog introduces Mercado Libre's AI platform, Verdi, which utilizes GPT-4 models to streamline processes like customer service and logistics. Verdi enhances productivity by autonomously handling complex tasks, improving efficiency across Mercado Libre's operations.🛠️ Platform Showdown: Comparing ML Tools & Services➽ How to Compute Moving Averages Using NumPy? This blog explains how to compute various types of moving averages using NumPy, including Simple Moving Average (SMA), Cumulative Moving Average (CMA), and Exponential Moving Average (EMA), commonly used in time-series analysis and financial forecasting.➽ Getting Started with Llamafactory: Installation and Setup Guide. This blog provides a guide on using LlamaFactory, an open-source tool for simplifying LLM training. It supports pretraining, fine-tuning, and RLHF methods, offering an easy setup for various models and training techniques.➽ Minnesota’s Enterprise Translation Office uses ChatGPT to bridge language gaps: Minnesota's Enterprise Translations Office (ETO) uses ChatGPT to provide faster, accurate, and equitable translation services for non-English-speaking residents. By incorporating AI, ETO improves accessibility to public services and addresses cultural relevance.➽ Optimizing Inventory Management with Reinforcement Learning: A Hands-on Python Guide. This blog explains the use of reinforcement learning (RL) for inventory management, specifically using Q-learning. It explores how RL can help optimize ordering policies by learning from data, removing the need for predefined demand models, and balancing inventory costs and demand uncertainty.➽ What Makes a True AI Agent? Rethinking the Pursuit of Autonomy: This blog critiques the hype around AI agents, emphasizing the need for a practical framework to assess agentic behavior. It argues for a spectrum-based approach, highlighting key attributes like perception and interactivity while questioning the true value of fully autonomous AI systems.➽ Why Your Service Engineers Need a Chatbot? This article explains how to build a chatbot using Gemini to assist service engineers with troubleshooting appliances. It highlights challenges with Retrieval-Augmented Generation (RAG) for handling manuals and explores Gemini's advanced features, like context caching and multimodal prompting, integrated into a Streamlit interface.➽ Could Conversational AI-Driven Data Analytics Finally Solve the Data Democratization Riddle? This article explores the potential of conversational AI-driven data analytics, sparked by tools like ChatGPT and Code Interpreter, to democratize data access. However, challenges remain in achieving enterprise-wide solutions for non-technical users.📊 Success Stories: Real-World ML Case Studies➽ MALPOLON: An AI Framework Advancing Species Distribution Modeling with Geospatial Data and Deep Learning. Species distribution modeling (SDM) has evolved from basic statistical methods to advanced machine-learning techniques. The MALPOLON framework, a Python-based deep learning tool, simplifies SDM by integrating multimodal data and improving scalability, accuracy, and accessibility for ecological research.➽ AMD Unveils AMD-135M: Its First Small Language Model Series, Trained on MI250 Accelerators with 670B Tokens. AMD has introduced AMD-135M, a language model with 135 million parameters optimized for its MI250 GPUs. Built on LLaMA2 architecture, it excels in text generation and language comprehension, leveraging datasets like SlimPajama and Project Gutenberg for pretraining.➽ MassiveDS: A 1.4 Trillion-Token Datastore Boosting Efficiency and Accuracy in Knowledge-Intensive NLP Applications. Recent research highlights the benefits of retrieval-based language models (RIC-LMs) that access external datastores during inference. Using the MassiveDS datastore, these models outperform larger parametric models, improving accuracy and efficiency across various tasks.➽ Announcing Vertex AI Prompt Optimizer: Vertex AI Prompt Optimizer simplifies prompt design by automatically optimizing instructions and demonstrations for different models, addressing the challenge of transferring prompts between LLMs. It enhances performance, supports various tasks, and tailors optimization to specific metrics.➽ Achieve operational excellence with well-architected generative AI solutions using Amazon Bedrock: Large enterprises face challenges in scaling generative AI while ensuring data privacy, security, compliance, and operational efficiency. This post highlights AWS's guidance, emphasizing Amazon Bedrock's role in securely integrating generative AI, managing risks, and driving innovation across organizations.🌍 ML Newsflash: Latest Industry Buzz & Discoveries➽ Ovis-1.6: An Open-Source MLLM Aligning Visual and Textual Embeddings. Ovis 1.6 is a multimodal large language model that structurally aligns visual and textual embeddings, overcoming traditional alignment challenges. It outperforms competitors in complex multimodal tasks like visual question answering and image captioning.➽ Logic-of-Thought: Boosting Logical Reasoning in Large Language Models with Propositional Logic. Large Language Models (LLMs) struggle with complex reasoning tasks. Logic-of-Thought (LoT) is a new method that enhances LLMs' reasoning by extracting, expanding, and translating logical expressions into natural language, improving performance across multiple reasoning datasets.➽ Instructive Decoding (ID): Enhancing Instruction-Tuned LLMs' Focus on Instructions Without Parameter Updates. Instructive Decoding (ID) enhances instruction-tuned language models by using "noisy instructions" to contrast predictions and improve performance on unseen tasks. This method boosts accuracy without parameter updates, improving generalization and task adherence.➽ NotebookLM Introduces Audio and YouTube Integration, Enhances Audio Overview Sharing: Google's NotebookLM has been enhanced to process audio and YouTube videos, expanding its research capabilities. By transcribing and summarizing multimedia content, it simplifies extracting key points, making research more efficient and comprehensive.➽ Google Releases FRAMES: A Dataset to Test RAG Applications on Factuality, Retrieval Accuracy, and Reasoning. This blog discusses Retrieval-Augmented Generation (RAG), a method combining retrieval mechanisms with generative models to improve factual accuracy and reasoning. It introduces the FRAMES dataset to evaluate RAG's performance in handling complex, multi-document queries.We’ve got more great things coming your way—see you soon!*{box-sizing:border-box}body{margin:0;padding:0}a[x-apple-data-detectors]{color:inherit!important;text-decoration:inherit!important}#MessageViewBody a{color:inherit;text-decoration:none}p{line-height:inherit}.desktop_hide,.desktop_hide table{mso-hide:all;display:none;max-height:0;overflow:hidden}.image_block img+div{display:none}sub,sup{line-height:0;font-size:75%} @media (max-width: 100%;display:block}.mobile_hide{min-height:0;max-height:0;max-width: 100%;overflow:hidden;font-size:0}.desktop_hide,.desktop_hide table{display:table!important;max-height:none!important}}

AutoGluon: Open-Source AutoML library, Heatmaps for Time SeriesSubscribe | Submit a tip | Advertise with usThe Future of AI & Data is Unfolding, Here’s What You Need to Know in DataPro #131!This week’s edition is packed with AI breakthroughs, data strategy debates, and hands-on tools to elevate your workflow. LlamaIndex is now part of the Gen AI Toolbox for Databases, streamlining AI-powered queries, while AutoGluon makes AutoML more accessible than ever. Meanwhile, the Platform-Mesh vs. Hub-and-Spoke vs. Centralized data team debate heats up, what’s the right structure for scaling AI?AI observability is the next big frontier, 2026 will mark a turning point as businesses move beyond experimentation to large-scale deployment. We also explore AWS & NVIDIA’s generative AI impact, how EliseAI is revolutionizing housing & healthcare, and why spurious regression in time series analysis remains a critical challenge.For hands-on practitioners, we’re covering heatmaps for time series, advanced DBeaver SQL tips, and a guide to integrating Google Analytics 4 with Amazon Redshift using AppFlow. Plus, the latest on Elon Musk’s lawsuit against OpenAI, and why the courts aren’t buying his claims.Keep scrolling for the full scoop!Cheers,Merlyn ShelleyGrowth Lead, Packt📚 Limited-Time Offer: 30% Off Bestselling eBooks!Buy NowBuy NowBuy NowBuy Now🔍 Fresh Insights ⋆✴︎˚｡⋆🔹 LlamaIndex is on Gen AI Toolbox for Databases: Google Cloud announced the integration of LlamaIndex with Gen AI Toolbox for Databases, an open-source server simplifying AI tool management for databases. LlamaIndex enhances AI agent development by structuring data and enabling powerful query engines. This collaboration streamlines security, scaling, and deployment for AI applications.🔹 Building Agentic Application Using Streamlit and Langchain: This guide explains how to build an agentic application using Streamlit and LangChain by integrating AI agents for answering queries, web searches, computations, and data visualization. It leverages Tavily Search, Python REPL, and Llama 3.3 LLM to create an interactive AI-driven workflow.🔹 Do I Need to Learn MicroPython as a Data Scientist? MicroPython is a lightweight version of Python optimized for microcontrollers and constrained environments. Data scientists can benefit from it for IoT, edge computing, prototyping, and robotics. As AI integrates with hardware, learning MicroPython can enhance data collection and processing capabilities.🔹 Getting Started with AutoGluon: Your First Steps in Automated Machine Learning: This blog introduces AutoGluon, an open-source AutoML library that simplifies machine learning by automating model selection, hyperparameter tuning, and ensembling. It walks through installation, training a model on the Titanic dataset, evaluating performance, and making predictions, making AutoML accessible for beginners.🔹 Build Your First Python Extension for VS Code in 7 Easy Steps: This blog provides a step-by-step guide to building a custom Python extension for VS Code. It covers setting up the environment, writing extension logic, testing, packaging, and publishing the extension to the marketplace, making it easy for developers to enhance their IDE.📚 Limited-Time Offer: 30% Off Bestselling eBooks!Buy NowBuy NowBuy NowBuy Now🚀 Trendspotting: What's Next in Tech Trends🔹 Reduce cost and improve your AI workloads: This blog provides five practical tips to optimize AI workloads on Google Cloud, covering platform selection, inference startup time, storage solutions, resource reservations, and custom disk images. It helps developers improve efficiency, reduce costs, and streamline AI model deployment and training processes.🔹 The Impact of GenAI and Its Implications for Data Scientists: Anthropic’s study on Claude.ai conversations reveal how GenAI is transforming workplaces, especially in data science. Rather than replacing jobs, GenAI enhances productivity by augmenting tasks. The blog emphasizes the importance of adaptability, critical thinking, and collaboration skills in the evolving AI landscape.🔹 Mastering Prompt Engineering with Functional Testing: A Systematic Guide to Reliable LLM Outputs: Functional testing in prompt engineering provides a structured approach to optimizing LLM outputs. By automating validation, running multiple iterations, and using algorithmic scoring, this method enhances reliability, reduces trial-and-error, and ensures consistent, accurate responses for complex AI workflows and tasks.🔹 Effortless Spreadsheet Normalisation With LLM: Large Language Models (LLMs) automate spreadsheet normalization by analyzing structure, estimating schemas, and generating transformation code. This improves data quality, tidiness, and usability. A structured workflow ensures efficiency, accuracy, and adaptability, enabling seamless machine-readable formats for better insights and analysis.🔹2026 Will Be the Year of Data + AI Observability: The blog observes that 2026 will be the tipping point for data + AI observability, as enterprise AI moves from experimentation to large-scale deployment. Key challenges include data readiness, system sprawl, feedback loops, and cost concerns. Without a standardized architecture, teams struggle to maintain reliability while integrating structured and unstructured data, AI models, and SaaS systems. Observability must be end-to-end, covering data, system performance, and AI outputs. Organizations with strong foundations in data reliability will gain a competitive edge, while those lacking observability risk inefficiency, poor AI performance, and potential failure in an evolving AI landscape.🔹 Ingest data from Google Analytics 4 and Google Sheets to Amazon Redshift using Amazon AppFlow: This blog explains how to ingest data from Google Analytics 4 (GA4) and Google Sheets into Amazon Redshift using Amazon AppFlow. It covers setting up data flows, configuring authentication, and establishing a seamless integration for efficient data analysis in Redshift.🛠️ Platform Showdown: Comparing ML Tools & Services🔹7 Powerful DBeaver Tips and Tricks to Improve Your SQL Workflow: This blog shares seven practical DBeaver tips to enhance your SQL workflow. It covers hidden features like the command palette, custom SQL formatting, column statistics, SQL templates, advanced copying options, and more to improve efficiency when working with databases.🔹 The court rejects Elon’s latest attempt to slow OpenAI down: This blog discusses the court’s rejection of Elon Musk’s attempt to hinder OpenAI, highlighting his alleged self-interest. It refutes claims about OpenAI’s structure, defends its nonprofit mission, and criticizes Musk’s legal tactics while reaffirming OpenAI’s commitment to long-term public benefit.🔹 How to Develop Complex DAXExpressions: This blog explores best practices for developing complex DAX expressions in Power BI. It emphasizes understanding requirements, defining logic, and managing filter contexts. Using step-by-step examples, it demonstrates how to build and refine calculations for accurate, scalable data analysis.🔹 From innovation to impact: How AWS and NVIDIA enable real-world generative AI success. This blog explores how AWS and NVIDIA enable real-world generative AI adoption at scale. It highlights customer success stories across industries, emphasizing infrastructure, optimization strategies, and the role of domain-specific AI in transforming workflows, healthcare, and enterprise applications with reliable, high-performance AI solutions.📊 Success Stories: Real-World ML Case Studies🔹 Heatmaps for Time Series: This blog explores how heatmaps visualize time series data, focusing on trends and outliers using non-linear color scales. It recreates the WSJ’s measles heatmap with Python’s Matplotlib, demonstrating data preprocessing, colormap design, and effective visualization techniques for analyzing and communicating complex datasets.🔹 Platform-Mesh, Hub and Spoke, and Centralised | 3 Types of data team: This blog explores three data team structures, Centralized, Hub-and-Spoke, and Platform Mesh, highlighting their impact on data and AI success. It explains how organizations evolve from centralized control to decentralized collaboration, emphasizing visibility, governance, and efficiency in scaling AI-driven workflows across teams.🔹 Linear Regression in Time Series: Sources of Spurious Regression. This blog explores the issue of spurious regression in time series analysis, highlighting how autocorrelated errors can lead to misleading statistical results. It explains key concepts like random walks, ARIMA processes, and Durbin-Watson statistics, using Python simulations to illustrate and prevent erroneous conclusions.🔹 EliseAI improves housing and healthcare efficiency with AI: This blog features an interview with EliseAI CEO Minna Song on how AI improves efficiency in housing and healthcare. It discusses AI adoption strategies, key technical breakthroughs, success metrics, and how the company stays competitive in a rapidly evolving AI landscape.We’ve got more great things coming your way, see you soon!*{box-sizing:border-box}body{margin:0;padding:0}a[x-apple-data-detectors]{color:inherit!important;text-decoration:inherit!important}#MessageViewBody a{color:inherit;text-decoration:none}p{line-height:inherit}.desktop_hide,.desktop_hide table{mso-hide:all;display:none;max-height:0;overflow:hidden}.image_block img+div{display:none}sub,sup{font-size:75%;line-height:0} @media (max-width: 100%;display:block}.mobile_hide{min-height:0;max-height:0;max-width: 100%;overflow:hidden;font-size:0}.desktop_hide,.desktop_hide table{display:table!important;max-height:none!important}}

Data Management Strategy at Microsoft, Building Multimodal Search Agents with BLIP-2 and Gemini👋 Hello ,📢 Welcome toDataPro #128~ Your Weekly Dose of Data Science & ML Innovation!The world of AI, machine learning, and data science never slows down, and neither do we! This week’s edition is packed with breakthroughs, must-know tools, and career insights to keep you ahead of the curve.🔹 Data & ML Reads: Explore Python Machine Learning By Example, Power BI mastery, deep reinforcement learning, and high-performance data manipulation with Polars.🔍 Fresh Insights: A 27-day AI coding experiment, deep dive into LLMs, and why data scientists should embrace Docker.🚀 Tech Trends: Advanced Time Intelligence in DAX, Multimodal search with BLIP-2 & Gemini, and Sparse Autoencoders in LLMs.🛠️ ML Tool Showdown: Discover MoBA’s new attention mechanism, Microsoft’s Magma AI for robotics & UI, and Mistral Saba’s breakthrough in Arabic & Tamil NLP.📊 Success Stories: Free interactive data visualizations with Marimo, SQLite-powered RAG, and how Decision Intelligence is shaping the future of data.💡 Your AI & ML Knowledge Hub is Here! Dive into these game-changing trends, tools, and innovations.🔗 Read it all now! ⬇️Cheers,Merlyn ShelleyGrowth Lead, Packt📚 Packt Signature Series: New Releases You Can't Miss❯❯❯❯ Python Machine Learning By Example: Written by Yuxi (Hayden) Liu, Python Machine Learning by Example, Fourth Edition is a hands-on guide covering NLP transformers, PyTorch, computer vision, and deep learning. It emphasizes best practices for building and improving real-world machine learning models using Python.Buy eBook $36.99 $24.99❯❯❯❯ Microsoft Power BI Cookbook: Written by Greg Deckler and Brett Powell, Microsoft Power BI Cookbook (3rd Edition) is a detailed guide for data professionals, covering data integration, Hybrid tables, scorecards, real-time processing, governance, security, and advanced visualization. With step-by-step techniques, it helps you transform raw data into actionable insights using Power BI’s latest innovations.Buy eBook $43.99 $29.99❯❯❯❯Modern Time Series Forecasting with Python: Written by Manu Joseph and Jeffrey Tackes, Modern Time Series Forecasting with Python (2nd Edition) is a detailed guide for data professionals, covering machine learning, deep learning, transformers, probabilistic forecasting, feature engineering, and ensemble methods. With hands-on techniques, it helps you build, evaluate, and deploy advanced forecasting models using Python, PyTorch, and pandas.Buy eBook $46.99 $31.99❯❯❯❯ Deep Reinforcement Learning Hands-On: Written by Maxim Lapan, Deep Reinforcement Learning Hands-On (3rd Edition) is a detailed guide to mastering RL, covering Q-learning, DQNs, PPO, RLHF, MuZero, and transformers. With hands-on projects, it helps machine learning professionals build, train, and apply RL models using PyTorch for real-world tasks in gaming, finance, and beyond.Buy eBook $46.99 $31.99❯❯❯❯ Polars Cookbook: Written by Yuki Kakegawa, Polars Cookbook is a hands-on guide featuring 60+ real-world projects to master data manipulation, transformation, and analysis with Python Polars. Covering advanced querying, performance optimization, and integrations with pandas, PyArrow, and cloud platforms, this book helps data professionals build fast, scalable, and efficient workflows.Buy eBook $46.99 $31.99❯❯❯❯ Python Feature Engineering Cookbook: Written by Galli, Python Feature Engineering Cookbook (3rd Edition) is a practical guide featuring real-world techniques to craft powerful features for tabular, transactional, and time-series data. Covering imputation, encoding, transformation, feature extraction, and automation, this book helps data professionals build efficient, reproducible, and production-ready feature engineering pipelines.Buy eBook $35.99 $24.99❯❯❯❯ Data Management Strategy at Microsoft: Written by Aleksejs Plotnikovs, Data Management Strategy at Microsoft is a practical guide to building a data-driven culture and maximizing data’s business value. Covering data strategy, governance, change management, and intellectual property, it provides key insights from Microsoft’s decade-long transformation to help leaders drive impactful data initiatives.Buy eBook $31.99 $21.99🔍 Fresh Insights ⋆✴︎˚｡⋆❯❯❯❯ Zero Human Code: What I Learned from Forcing AI to Build (and Fix) Its Own Code for 27 Straight Days: This blog explores a 27-day experiment where AI tools handled all coding, debugging, and implementation while the author acted solely as an orchestrator. It reveals the real limitations of AI-driven development, challenges in guiding AI, and key insights into prompting, system complexity, and architectural rigidity.❯❯❯❯ How LLMs Work: Pre-Training to Post-Training, Neural Networks, Hallucinations, and Inference: This blog provides a deep dive into how large language models (LLMs) work, covering their pre-training, post-training, neural network mechanics, inference, and hallucinations. It explains how LLMs are built, trained, fine-tuned, and optimized for real-world applications.❯❯❯❯ Why Data Scientists Should Care about Containers and Stand Out with This Knowledge: This blog explains why data scientists should understand containers, particularly Docker, to enhance model deployment, reproducibility, cloud integration, and scalability. It covers key concepts, practical applications, and provides a beginner-friendly guide to setting up a Jupyter Notebook in a Docker container.🚀 Trendspotting: What's Next in Tech Trends❯❯❯❯ Advanced Time Intelligence in DAX with Performance in Mind: This blog explores advanced time intelligence techniques in DAX, focusing on handling complex date-related calculations while optimizing performance. It covers scenarios like last N periods, leap years, week-to-date sums, and fiscal week YTD, using an extended date table for efficiency.❯❯❯❯ Multimodal Search Engine Agents Powered by BLIP-2 and Gemini: This blog explores how multimodal search engine agents powered by BLIP-2 and Gemini enhance e-commerce by enabling text and image-based searches. It explains BLIP-2’s architecture, training process, and loss functions, demonstrating its application in a fashion assistant for improved product discovery.❯❯❯❯ Formulation of Feature Circuits with Sparse Autoencoders in LLM: This blog explores how sparse autoencoders help disentangle feature circuits in large language models (LLMs), focusing on subject-verb agreement. It demonstrates how an LLM processes grammatical rules, visualizing feature circuits in both toy models and GPT-2 to enhance interpretability and debugging.🛠️ Platform Showdown: Comparing ML Tools & Services❯❯❯❯Moonshot AI Research Introduce Mixture of Block Attention (MoBA): A New AI Approach that Applies the Principles of Mixture of Experts (MoE) to the Attention Mechanism. This blog introduces Mixture of Block Attention (MoBA), a new AI approach that applies Mixture of Experts (MoE) principles to Transformer attention. MoBA improves efficiency in long-context processing by learning which token blocks to focus on, reducing computational costs while maintaining performance.❯❯❯❯ Microsoft Researchers Present Magma: A Multimodal AI Model Integrating Vision, Language, and Action for Advanced Robotics, UI Navigation, and Intelligent Decision-Making. This blog introduces Magma, a multimodal AI model by Microsoft Research that integrates vision, language, and action for robotics, UI navigation, and intelligent decision-making. Magma outperforms existing models by combining deep learning architectures, spatial reasoning, and large-scale pretraining for superior multimodal task execution.❯❯❯❯ Mistral AI Introduces Mistral Saba: A New Regional Language Model Designed to Excel in Arabic and South Indian-Origin Languages such as Tamil. This blog introduces Mistral Saba, a 24-billion-parameter AI model designed by Mistral AI to enhance Arabic and South Indian-origin languages like Tamil. With advanced NLP techniques and regional training, Mistral Saba delivers efficient, context-aware, and cost-effective AI solutions for diverse dialects and cultural nuances.📊 Success Stories: Real-World ML Case Studies❯❯❯❯Publish Interactive Data Visualizations for Free with Python and Marimo: This blog explores Marimo, a newly released Python library for publishing interactive data visualizations without the need for costly servers. Combining the ease of Jupyter notebooks with Pyodide/WASM, Marimo allows data scientists to create and share interactive web-based visualizations seamlessly and for free.❯❯❯❯ Roadmap to Becoming a Data Scientist, Part 4: Advanced Machine Learning. This blog explores advanced machine learning skills essential for data scientists, covering NLP, computer vision, reinforcement learning, and optimization techniques like fine-tuning and quantization. It emphasizes the evolution of ML methods, key concepts in LLMs, embeddings, and time series analysis, and strategies to stay competitive in the fast-changing AI landscape.❯❯❯❯ Retrieval Augmented Generation in SQLite: This blog explores Retrieval-Augmented Generation (RAG) with SQLite, showing how to perform vector search and generative AI integration using only SQLite, the sqlite-vec extension, and OpenAI embeddings, without relying on cloud vector databases. It provides a step-by-step guide to setting up a single-file RAG system, covering virtual tables, embeddings, and querying techniques for efficient, lightweight AI applications.❯❯❯❯ The Future of Data: How Decision Intelligence is Revolutionizing Data: This blog explores Decision Intelligence (DI), a rapidly growing field that combines AI, data science, and behavioral sciences to improve decision-making. It explains how DI differs from AI, its practical applications, and how organizations can leverage it for better predictions, automation, and efficiency across industries like retail, healthcare, finance, and manufacturing.We’ve got more great things coming your way, see you soon!*{box-sizing:border-box}body{margin:0;padding:0}a[x-apple-data-detectors]{color:inherit!important;text-decoration:inherit!important}#MessageViewBody a{color:inherit;text-decoration:none}p{line-height:inherit}.desktop_hide,.desktop_hide table{mso-hide:all;display:none;max-height:0;overflow:hidden}.image_block img+div{display:none}sub,sup{font-size:75%;line-height:0} @media (max-width: 100%;display:block}.mobile_hide{min-height:0;max-height:0;max-width: 100%;overflow:hidden;font-size:0}.desktop_hide,.desktop_hide table{display:table!important;max-height:none!important}}

Hands-On Machine Learning with C++, Vertex AI Gen AI Evaluation Service, Biostatistics with Python🌟Share, Shape, & Claim Your Free Packt Credit! 📚We're looking for data professionals to join a quick 30-minute chat about their learning needs. The first 25 respondents in a data-specific role will have the opportunity to speak with our team, share their insights, and receive a free Packt credit to claim any eBook of their choice! Hurry – submit your interest now and keep an eye out for our team's meeting invite. You could be one of the chosen ones!👉 Reserve Your Interview SlotHyperproof's 6th Annual IT Risk and Compliance Benchmark Report ReleasedGRC is no longer just a checkbox, it’s a competitive advantage.Hyperproof’s6th Annual IT Risk & Compliance Benchmark Reportreveals a major shift: organizations are maturing their GRC practices, centralizing teams, and increasing budgets. With91% of companies now prioritizing compliance, the landscape is evolving fast.The key takeaway?Governance, risk, and compliance are now drivers of operational excellence and strategic growth. Hyperproof’s industry insights and newGRC Maturity Modelequip organizations to stay ahead.📊Get thefull report& start building a stronger, more resilient GRC strategy today.Download the Report Now!Sponsored📢 Welcome to DataPro #126 ~ Your Weekly Dose of Data Science & ML Innovation!The world of data science and machine learning is advancing at lightning speed, and we’re here to keep you ahead of the curve! Whether it’s breakthrough AI frameworks, game-changing open-source tools, or must-know industry updates, this edition packs everything you need to stay informed, innovate, and lead in the ML space. 📚 New Releases You Can't Miss:✅Hands-On Machine Learning with C++ - Build smart models with modern C++ libraries.✅Biostatistics with Python - Apply Python to real-world biomedical & biotech projects.✅Data Engineering with Databricks Cookbook - Master Apache Spark, Delta Lake & Databricks.🔍 This Week’s Deep Dive:✅ Support Vector Machine (SVM) Algorithm - A fundamental yet powerful ML technique.✅ OpenAI’s Deep Research Agent -How it’s revolutionizing data-driven discovery.✅ Yandex’s Open-Source Perforator - Optimizing server performance like never before.✅ Meta AI’s MILS - A training-free multimodal AI framework pushing zero-shot learning to new heights.✅ No-Code ML with Amazon SageMaker Canvas - Predict heart disease with an intuitive workflow.✅ Vertex AI Gen AI Evaluation Service - A smarter way to assess and improve AI agents.🧠 Featured Insights:✅Mistral AI Releases Mistral-Small-24B-Instruct-2501 - A low-latency 24B-parameter model under Apache 2.0.✅Improving Agent Systems & AI Reasoning - Smarter, more reliable AI solutions.Whether you’re a data scientist, ML engineer, or AI enthusiast, DataPro keeps you informed, inspired, and ahead of the curve. Stay tuned for more updates next week!💡 Got a topic you'd love to see covered? Let us know! 🚀Cheers,Merlyn ShelleyGrowth Lead, Packt.📚 Packt Signature Series: New Releases You Can't Miss❯❯❯❯ Hands-On Machine Learning with C++:Written by Kirill Kolodiazhnyi, this book equips machine learning engineers with practical ML and deep learning techniques using modern C++ libraries. You will learn about model selection, tuning, and deployment on mobile and embedded devices, real-time object detection, transfer learning, MLflow for experiment tracking, and Optuna for hyperparameter tuning, providing a complete guide to building efficient ML systems. Start your free trial for access, renewing at $19.99/month.eBook $27.98 $39.99Print + eBook $49.99❯❯❯❯ Biostatistics with Python: Written by Darko Medin, this book simplifies biostatistics with Python through hands-on biomedical and biotechnology projects. You will learn about data cleaning, hypothesis testing, effect size analysis, predictive modeling, survival analysis, and meta-analysis, making it easier to apply statistical methods in biological research. With real-world case studies, this guide helps life science professionals and researchers confidently integrate biostatistical analysis into their work. Start your free trial for access, renewing at $19.99/month.eBook $18.99 $27.99Print + eBook $34.99❯❯❯❯ Data Engineering with Databricks Cookbook: Written by Pulkit Chadha, this cookbook provides a practical, recipe-based guide to mastering data engineering with Databricks, Apache Spark, and Delta Lake. You will learn about data ingestion, transformation, and optimization, as well as orchestrating pipelines, implementing DataOps/DevOps, and enforcing data governance with Unity Catalog. Designed for data engineers and practitioners, this book offers hands-on techniques to build scalable, high-performance data solutions in modern cloud environments. Start your free trial for access, renewing at $19.99/month.eBook $27.98 $39.99Print + eBook $49.99🔍 Fresh Insights, Trending Now on Medium ⋆✴︎˚｡⋆❯❯❯❯ Support Vector Machines: A Progression of Algorithms: This blog explores the Support Vector Machine (SVM) algorithm, a powerful tool for classification problems. It explains the progression from the Maximal Margin Classifier (MMC) to the Support Vector Classifier (SVC) and finally to SVM, highlighting how each step improves decision boundary flexibility and robustness.❯❯❯❯ Are Public Agencies Letting Open-Source Software Down? This blog explores the impact of open-source software on technology, innovation, and democracy. It highlights its role in AI advancements, geospatial mapping, and public collaboration. Through personal anecdotes and practical examples, it underscores how open access, transparency, and shared knowledge drive progress across industries and global communities.❯❯❯❯ Improving Agent Systems & AI Reasoning: This blog explores the rise of AI Agents and the limitations of large language models (LLMs) in reasoning. It examines how new Reasoning Language Models (RLMs), like DeepSeek-R1 and OpenAI’s o1 and o3, improve AI reasoning through post-training and test-time compute scaling, reshaping AI agent development.❯❯❯❯ What OpenAI’s Deep Research Means for the Future of Data Science? This blog introduces OpenAI’s Deep Research Agent, a tool designed to streamline complex data gathering and analysis for data scientists. It automates multi-step research, synthesizes information from diverse sources, ensures accuracy with verified citations, and enhances efficiency in problem-solving across domains like healthcare, finance, and AI development.🚀 Trendspotting: What's Next in Tech Trends❯❯❯❯ Meta AI Introduces MILS: A Training-Free Multimodal AI Framework for Zero-Shot Image, Video, and Audio Understanding. This blog introduces Meta AI’s MILS, a training-free multimodal AI framework that enables large language models (LLMs) to perform image, video, and audio reasoning without task-specific training. Using an iterative optimization process with a generator and scorer, MILS enhances zero-shot performance across diverse modalities, improving multimodal AI adaptability.❯❯❯❯ 4 Open-Source Alternatives to OpenAI’s $200/Month Deep Research AI Agent. This blog explores four open-source AI research agents that serve as cost-effective alternatives to OpenAI’s Deep Research AI Agent. These tools leverage advanced search, extraction, and reasoning capabilities, offering researchers customizable, self-hostable solutions for automating in-depth research without the high cost of proprietary AI systems.❯❯❯❯ Mistral AI Releases the Mistral-Small-24B-Instruct-2501: A Latency-Optimized 24B-Parameter Model Released Under the Apache 2.0 License: This blog introduces Mistral-Small-24B-Instruct-2501, a compact yet high-performing language model designed for efficiency and accessibility. With 24 billion parameters, multilingual capabilities, and a 32k context window, it rivals larger models like Llama 3 while supporting local deployment and open-source flexibility under the Apache 2.0 license.❯❯❯❯ Yandex Develops and Open-Sources Perforator: An Open-Source Tool that can Save Businesses Billions of Dollars a Year on Server Infrastructure. This blog introduces Perforator, an open-source tool from Yandex designed for real-time server and application performance monitoring. By identifying resource-intensive code and enabling profile-guided optimization, Perforator helps businesses cut infrastructure costs by up to 20%, making it a powerful solution for efficiency and scalability.🛠️ Platform Showdown: Comparing ML Tools & Services❯❯❯❯ Advances to low-bit quantization enable LLMs on edge devices: This blog explores advancements in low-bit quantization for deploying large language models (LLMs) on edge devices. Microsoft Research introduces T-MAC, Ladder, and LUT Tensor Core, three solutions optimizing mixed-precision matrix multiplication (mpGEMM) to improve AI efficiency. These innovations enhance model performance, reduce memory demands, and enable real-time AI processing on resource-constrained hardware.❯❯❯❯ Trellix lowers cost, increases speed, and adds delivery flexibility with cost-effective and performant Amazon Nova Micro and Amazon Nova Lite models: This blog explores how Trellix Wise, an AI-powered cybersecurity platform, integrates Amazon Nova Micro to enhance threat investigation speed and cost efficiency. By leveraging generative AI and Retrieval-Augmented Generation (RAG), Trellix automates security event analysis, reducing investigation time while maintaining accuracy, improving scalability, and optimizing operational costs.❯❯❯❯ OfferUp improved local results by 54% and relevance recall by 27% with multimodal search on Amazon Bedrock and Amazon OpenSearch Service: This blog explores how OfferUp modernized its search architecture by adopting Amazon Titan Multimodal Embeddings and Amazon OpenSearch Service. By integrating multimodal search, OfferUp improved search relevance, user engagement, and local discovery, enabling users to search with both text and images for a more intuitive marketplace experience.❯❯❯❯ Use generative AI on AWS for efficient clinical document analysis: This blog explores how Clario leverages generative AI on AWS to streamline clinical trial document analysis. By integrating Amazon Textract, OpenSearch, Bedrock, and SageMaker, Clario automates parsing, retrieval, classification, and analysis, significantly reducing review time and accelerating drug development while maintaining regulatory compliance.❯❯❯❯ Build a multi-interface AI assistant using Amazon Q and Slack with Amazon CloudFront clickable references from an Amazon S3 bucket: This blog explores how Amazon Q Business and Slack enable multi-interface AI assistants for seamless user interaction. By integrating Retrieval Augmented Generation (RAG) with Amazon Kendra and CloudFront, organizations can enhance AI accessibility, provide context-aware responses, and improve productivity without requiring users to switch applications.📊 Success Stories: Real-World ML Case Studies❯❯❯❯ No-Code ML Approach to Predict Heart Disease with Amazon SageMaker Canvas: This blog explores how Amazon SageMaker Canvas enables no-code predictive modeling for heart disease detection. By integrating SageMaker Data Wrangler for data preparation and machine learning for classification, healthcare professionals can analyze biomedical data, identify key indicators, and improve early diagnosis without extensive coding expertise.❯❯❯❯ OpenAI Introducing data residency in Europe: This blog introduces data residency in Europe for ChatGPT Enterprise, ChatGPT Edu, and the API Platform, enhancing data sovereignty compliance for organizations. OpenAI ensures secure, private AI usage with in-region data processing, encryption, and GDPR compliance, empowering businesses and institutions across Europe to integrate AI confidently.❯❯❯❯ Create a 360-degree master data management patient view solution using Amazon Neptune and generative AI: This blog explores how Amazon Neptune and generative AI enable a 360-degree patient view, integrating electronic health records (EHRs), lab results, prescriptions, and social determinants. By unifying healthcare data, providers can enhance personalized care, improve early disease detection, and support clinical decision-making, leading to better patient outcomes.❯❯❯❯ Build a brand logo with Imagen 3 and Gemini: This post explores how Imagen 3, Gemini, and the Python Library Pillow work together to help businesses create branded marketing visuals. Using AI-powered image generation, selection, and integration, companies can design unique brand identities and logos tailored to their aesthetic. Learn how this AI workflow can enhance your creative process and deliver high-quality promotional visuals efficiently.❯❯❯❯ Evaluate your AI agents with Vertex Gen AI evaluation service: Vertex AI Gen AI Evaluation Service is now in public preview, enabling rigorous AI agent assessment. It offers final response and trajectory analysis metrics to improve decision-making. Compatible with LangChain, LangGraph, CrewAI, and Google Cloud services, it supports native agent inference and automatic logging in Vertex AI Experiments.We’ve got more great things coming your way, see you soon!*{box-sizing:border-box}body{margin:0;padding:0}a[x-apple-data-detectors]{color:inherit!important;text-decoration:inherit!important}#MessageViewBody a{color:inherit;text-decoration:none}p{line-height:inherit}.desktop_hide,.desktop_hide table{mso-hide:all;display:none;max-height:0;overflow:hidden}.image_block img+div{display:none}sub,sup{font-size:75%;line-height:0} @media (max-width: 100%;display:block}.mobile_hide{min-height:0;max-height:0;max-width: 100%;overflow:hidden;font-size:0}.desktop_hide,.desktop_hide table{display:table!important;max-height:none!important}}

40+ Cool AI Tools, Inheritune, Rhymes AI’s Aria, Create Podcasts with NotebookLM, Falcon 2 11BLooking to build, train, deploy, or implement Generative AI?Meet Innodata — offering high-quality solutions for developing and implementing industry-leading generative AI, including:➤ Diverse Golden Datasets➤ Supervised Fine-Tuning Data➤ Human Preference Optimization (e.g. RLHF)➤ RAG Development ➤ Model Safety, Evaluation, & Red Teaming ➤ Data Collection, Creation, & Annotation ➤ Prompt Engineering With 5,000+ in-house SMEs and expansion and localization supported across 85+ languages,Innodata drives AI initiatives for enterprises globally.Learn More!SponsoredWelcome to DataPro #116 – Your Weekly Dose of Data Magic! 🌟Stay at the cutting edge of data engineering, data science, and AI! This week’s newsletter delivers the latest tools, insights, and strategies you need to accelerate your workflow, fine-tune your models, and power your innovations. From optimizing pipelines to mastering AI trends, we’ve got you covered. Let’s get started! 🚀🚨 Packt Conference Alert! 🚨Stay at the forefront of AI innovation! 🚀 Join us for 3 action-packed days of LIVE sessions with 20+ top experts and unleash the full power of Generative AI at our upcoming conference. Don’t miss out - Claim your spot today!🔍 Spotlight Algorithm: This Week's Must-Know Model✦ Un Ministral, des Ministraux: Mistral AI’s new Ministral 3B and 8B models✦ MIBench: The Ultimate AI Benchmark for Model Inversion Attacks & Defenses✦ OPEN-RAG: Revolutionizing Reasoning with Open-Source LLMs✦ Inheritune: Smarter, Smaller Language Models with Efficient AI Training✦ OpenAI’s MLE-Bench: A Deep Dive into ML Engineering Agent Performance✦ OpenAI Update: Disrupting Misuse and Strengthening AI Ethics🚀 Tech Buzz: What’s Trending in AI?✦ BigQuery x Apache Iceberg: Next-Gen Data Storage, Unlocked✦ Meet Arch: The Intelligent Gateway for Seamless LLM Integration✦ MRAG-Bench: A Vision-Centric AI Benchmark for Multimodal Models✦ Adaptive Computation: MIT's Smarter, Cost-Efficient Language Models✦ LoLCATS: Stanford’s Efficient LLM Linearization Breakthrough🛠️ Tool Time: Top ML Tools & Services✦ 40+ Cool AI Tools You Can't Miss in October✦ Zyphra's Zamba2-7B: Power-Packed Small Language Model✦ OpenR: An Open-Source Framework for LLM Reasoning✦ SuperNova-Medius: A 14B Model Shaking Up AI✦ Aria: Rhymes AI’s State-of-the-Art Multimodal MoE Model📊 ML in Action: Success Stories✦ NVIDIA’s MoE Models: Upcycling LLMs for Greater Efficiency✦ Google’s Tx-LLM: Fine-Tuned AI for Therapeutic Advancements✦ INTELLECT-1: Pioneering Decentralized AI Model Training✦ HyperAgent: FPT AI’s Generalist Agent Excelling in Software Engineering🌍 ML Newsflash: Fresh Off the AI Press✦ Create Podcasts with NotebookLM: Your Educational Content, Now Audio!✦ YouTube Study Guides: Turn Videos into Learning Powerhouses with NotebookLM✦ Claude AI: A Deep Dive into Anthropic’s AI Assistant & Artifacts✦ ML Deployment 101: Cloud vs. Edge—Which Strategy Wins?✦ lintsampler: Quick Sampling from Any Distribution, Simplified✦ Falcon 2 11B on EC2: A Guide to Efficient Model InferenceThere you have it—this week's freshest insights to keep you ahead in the ever-evolving world of Data and ML! Keep innovating, stay curious, and we’ll see you next week with more DataPro magic! 🎩✨Take our weekly survey and get a free PDF copy of our best-selling book,"Interactive Data Visualization with Python - Second Edition."We appreciate your input and hope you enjoy the book!Share Your Insights and Shine! 🌟💬Cheers,Merlyn Shelley,Editor-in-Chief, Packt.BOOK TODAY AT $239.99 $399.99JoinGenerativeAI InActionnow withaFull Event Pass for just $239.99—40% off the regular price—with codeFLASH40.Three Reasons Why You Cannot Miss This Event:1. Network with 25+ Leading AI Experts2. Gain Insights from 30+ Dynamic Talks and Hands-On Sessions3. Engage with Experts and Peers through 1:1 Networking, Roundtables, and AMAsAct fast—this FLASH SALE is only for a limited number of seats!CLAIM NOW - LIMITED SEATS📚 Packt Signature Series: Must-Reads & Author Insights➽ RAG-Driven Generative AI: This new title, RAG-Driven Generative AI, is perfect for engineers and database developers looking to build AI systems that give accurate, reliable answers by connecting responses to their source documents. It helps you reduce hallucinations, balance cost and performance, and improve accuracy using real-time feedback and tools like Pinecone and Deep Lake. By the end, you’ll know how to design AI that makes smart decisions based on real-world data—perfect for scaling projects and staying competitive! Start your free trial for access, renewing at $19.99/month.eBook $24.99 $35.99Print + eBook $29.99 $43.99➽ Building Production-Grade Web Applications with Supabase: This new book is all about helping you master Supabase and Next.js to build scalable, secure web apps. It’s perfect for solving tech challenges like real-time data handling, file storage, and enhancing app security. You'll even learn how to automate tasks and work with multi-tenant systems, making your projects more efficient. By the end, you'll be a Supabase pro! Start your free trial for access, renewing at $19.99/month.eBook $15.99 $31.99Print + eBook $27.98 $39.99➽ Python Data Cleaning and Preparation Best Practices: This new book is a great guide for improving data quality and handling. It helps solve common tech issues like messy, incomplete data and missing out on insights from unstructured data. You’ll learn how to clean, validate, and transform both structured and unstructured data—think text, images, and audio—making your data pipelines reliable and your results more meaningful. Perfect for sharpening your data skills! Start your free trial for access, renewing at $19.99/month.eBook $24.99 $35.99Print + eBook $30.99 $44.99🔍 Model Breakdown: Unveiling the Algorithm of the Week➽ Un Ministral, des Ministraux: Mistral AI introduces Ministral 3B and 8B models for edge computing, excelling in knowledge, reasoning, and efficiency. Designed for low-latency, privacy-first use cases, they support up to 128k context length, outperforming competitors while offering compute-efficient solutions for diverse applications.➽ MIBench: A Comprehensive AI Benchmark for Model Inversion Attack and Defense. The postdiscusses Model Inversion (MI) attacks, where attackers attempt to recreate sensitive training data from machine learning models. To address the lack of reliable benchmarks for comparing attacks and defenses, researchers introduced MIBench, a modular toolbox for evaluating MI methods, promoting more consistent, extensible research.➽ OPEN-RAG: A Novel AI Framework Designed to Enhance Reasoning Capabilities in RAG with Open-Source LLMs. This blog discusses Open-RAG, a novel framework designed to improve the reasoning and factual accuracy of retrieval-augmented generation (RAG) models using open-source large language models (LLMs). By transforming LLMs into efficient sparse mixture-of-experts models, Open-RAG excels in handling complex reasoning tasks while balancing accuracy and computational efficiency.➽ Inheritune: An Effective AI Training Approach for Developing Smaller and High-Performing Language Models. This blog discusses Inheritune, a method to train smaller, efficient language models by inheriting early layers from larger pre-trained models and progressively expanding them. Inheritune addresses attention degeneration in deeper layers, achieving performance comparable to larger models with fewer layers.➽ OpenAI’s MLE-bench: Evaluating Machine Learning Agents on Machine Learning Engineering. This blog introduces MLE-bench, a benchmark created by OpenAI to evaluate AI agents' machine learning engineering skills through 75 Kaggle competitions. The top-performing setup achieved a bronze medal level in 16.9% of competitions, with open-source code available for future research.➽ Update from OpenAI on disrupting deceptive uses of AI: This blog highlights OpenAI's efforts to prevent misuse of its models, particularly during global elections, by disrupting over 20 deceptive networks. It emphasizes ongoing work to enhance AI security and share insights with stakeholders and industry peers.🚀 Trendspotting: What's Next in Tech Trends➽ Announcing BigQuery tables for Apache Iceberg: This blog announces BigQuery tables for Apache Iceberg, a fully managed storage engine offering enterprise-level features like autonomous storage optimization and high-throughput streaming ingestion. It addresses challenges with open-source formats, enabling seamless data management and integration with Apache Spark and Flink.➽ Meet Arch: The Intelligent Layer 7 Gateway for LLM Applications. This blog introduces Arch, an intelligent Layer 7 gateway designed to enhance security, observability, and personalization for large language model (LLM) applications. Arch helps developers efficiently manage sensitive data, track performance, and personalize user interactions in real-time.➽ Researchers from UCLA and Stanford Introduce MRAG-Bench: An AI Benchmark Specifically Designed for Vision-Centric Evaluation for Retrieval-Augmented Multimodal Models. This blog introduces MRAG-Bench, a vision-centric benchmark designed to evaluate large vision-language models (LVLMs) in scenarios where visual knowledge outperforms textual information. It highlights gaps in current models' ability to leverage visual data, encouraging better multimodal understanding.➽ This AI Paper by MIT Introduces Adaptive Computation for Efficient and Cost-Effective Language Models: This blog discusses MIT's innovative approach to improve language model efficiency by adapting computation based on input complexity. Their method dynamically allocates resources, reducing computation by up to 50% without sacrificing performance, optimizing tasks in coding, math, and dialogues.➽ Stanford Researchers Propose LoLCATS: A Cutting Edge AI Method for Efficient LLM Linearization. This blog introduces LoLCATS, a method to efficiently linearize large language models by reducing memory and computational costs without sacrificing quality. Through attention transfer and low-rank adaptation, LoLCATS scales models like Llama 3 70B while maintaining high performance.🛠️ Platform Showdown: Comparing ML Tools & Services➽ 40+ Cool AI Tools You Should Check Out (Oct 2024): This blog highlights various AI tools designed to enhance productivity, creativity, and efficiency across multiple domains, including content creation, personalized media, website building, legal advising, business decision-making, and multimodal capabilities, offering innovative, time-saving solutions.➽ Zyphra Releases Zamba2-7B: A State-of-the-Art Small Language Model. Zyphra's newly released Zamba2-7B is a state-of-the-art small language model that outperforms competitors in quality and speed. Designed for environments with hardware limitations, it combines efficiency, innovative architecture, and open-source availability, democratizing advanced AI.➽ OpenR: An Open-Source AI Framework Enhancing Reasoning in Large Language Models. OpenR is an open-source framework designed to enhance large language models' reasoning abilities through reinforcement learning, process supervision, and advanced inference strategies. It improves reasoning performance in tasks like mathematics and coding, providing a collaborative platform for further advancements.➽ Arcee AI Releases SuperNova-Medius: A 14B Small Language Model Built on the Qwen2.5-14B-Instruct Architecture. SuperNova-Medius, a 14B parameter language model from Arcee AI, balances high performance with accessibility by rivaling larger models like 70B counterparts. It combines innovative optimization techniques for cost-effective, efficient deployment, making advanced AI more inclusive and sustainable.➽ Rhymes AI Released Aria: An Open Multimodal Native MoE Model Offering State-of-the-Art Performance Across Diverse Language, Vision, and Coding Tasks. Aria is an open-source multimodal AI model that integrates text, images, and videos, excelling in complex tasks with its fine-grained mixture-of-experts architecture. It offers competitive performance with lower computational costs, filling a critical gap in accessible multimodal AI.📊 Success Stories: Real-World ML Case Studies➽ NVIDIA AI Researchers Explore Upcycling Large Language Models into Sparse Mixture-of-Experts. Researchers from NVIDIA introduced a method to upcycle pre-trained dense models into Mixture of Experts (MoE) models, enhancing capacity and performance without increasing computational costs. Their technique, using virtual group initialization and softmax-then-topK routing, improved model accuracy and efficiency.➽ Google AI Introduces Tx-LLM: A Large Language Model (LLM) Fine-Tuned fromPaLM-2 to Predict Properties of Many Entities that are Relevant to Therapeutic Development. Tx-LLM, introduced by Google Research and DeepMind, is a fine-tuned large language model designed for diverse therapeutic tasks across drug development. Trained on 709 datasets, it excels in combining molecular and text features, outperforming state-of-the-art models in many tasks.➽ INTELLECT-1: The First Decentralized 10-Billion-Parameter AI Model Training. INTELLECT-1, launched by Prime Intellect AI, is a decentralized initiative to train a 10-billion-parameter AI model, inviting global participation. It challenges centralized AI development, promoting inclusivity, transparency, and collaboration in creating open-source artificial general intelligence (AGI).➽ FPT Software AI Center Introduces HyperAgent: A Groundbreaking Generalist Agent System to Resolve Various Software Engineering Tasks at Scale, Achieving SOTA Performance on SWE-Bench and Defects4J. HyperAgent, introduced by FPT Software AI Center, is a multi-agent system designed to handle a wide range of software engineering tasks. It mimics human developer workflows across phases like planning, code editing, and verification, offering generalizability, efficiency, and scalability.🌍 ML Newsflash: Latest Industry Buzz & Discoveries➽ How to Create Custom Educational Podcasts with NotebookLM? NotebookLM, an AI tool by Google, allows users to create podcasts from documents using two AI voices. These voices discuss the document's key points, making it sound like a real conversation. Users can upload content, customize podcasts, and adjust playback options.➽ How to Create YouTube Video Study Guides with NotebookLM? This blog explains how to use NotebookLM to create study guides from YouTube videos. By uploading video links, NotebookLM generates summaries, FAQs, and structured study materials, making it easier for students and educators to organize key points efficiently.➽ Claude AI: Unboxing Anthropic’s LLM-based AI Assistant, Artifacts & Use Cases. This blog introduces Claude AI, an advanced assistant developed by Anthropic. It highlights Claude's key features, including advanced visual reasoning and "artifacts," which are reusable content pieces that enhance collaborative workflows. Claude excels in business-oriented problem-solving and ethical AI interactions.➽ How to Choose the Best ML Deployment Strategy: Cloud vs. Edge? This blog explores the various methods of deploying machine learning models, emphasizing the differences between cloud and edge deployment. It covers cloud deployment methods like API, serverless, and batch processing, as well as edge deployment for native and web applications, offering pros, cons, and real-world examples.➽ lintsampler: a new way to quickly get random samples from any distribution: lintsampler is a Python package that simplifies and efficiently generates random samples from complex probability distributions. It offers an alternative to traditional methods like MCMC (Markov Chain Monte Carlo), providing an easy, fast, and adaptable approach for sampling across various dimensions and use cases.➽ Learn how to deploy Falcon 2 11B on Amazon EC2 c7i instances for model Inference: This blog introduces the Falcon 2 11B foundation model, developed by Technology Innovation Institute (TII), now deployable on Amazon EC2 c7i instances with Intel AMX support. It explores model quantization (INT8 and INT4) using OpenVINO for efficient, cost-effective real-time AI applications on CPUs.We’ve got more great things coming your way—see you soon!*{box-sizing:border-box}body{margin:0;padding:0}a[x-apple-data-detectors]{color:inherit!important;text-decoration:inherit!important}#MessageViewBody a{color:inherit;text-decoration:none}p{line-height:inherit}.desktop_hide,.desktop_hide table{mso-hide:all;display:none;max-height:0;overflow:hidden}.image_block img+div{display:none}sub,sup{line-height:0;font-size:75%} @media (max-width: 100%;display:block}.mobile_hide{min-height:0;max-height:0;max-width: 100%;overflow:hidden;font-size:0}.desktop_hide,.desktop_hide table{display:table!important;max-height:none!important}}

For a limited time, save on the best-selling books that will elevate your skills and knowledge! @media only screen and (max-width: 100%;} #pad-desktop {display: none !important;} } @media only screen and (max-width: 100%;} #pad-desktop {display: none !important;} }Transform Your GRC Program. No More Chasing Evidence.If you’re responsible for the GRC program in your organization, don't chase stakeholders for evidence.Use Anecdotes to continuously and indepandantly monitor your tech stack with credible GRC data. Whether you’re complying with SOX, NIST, PCI, or a custom framework, stop managing them in isolation. With Anecdotes’ advanced cross-mapping solution, you can reuse shared evidence across different scopes. Focus on strategy and strengthen your GRC program with Anecdotes.Let's TalkSponsored✨Welcome to Packt’s Signature Series: New Titles Just Arrived!📚We're thrilled to introduce the latest addition to our Signature Series—a curated collection of the best-selling titles in the data industry! This limited-time offer is packed with expert insights on mastering data science algorithms, Generative AI, and multimodal systems.For a limited time, enjoy a 30% discount on both ebook and print editions of these recommended titles. Don’t delay—this offer ends soon!Check out these handpicked titles that will help you solve complicated data problems:✦ RAG-Driven Generative AI ✦ The AI Value Playbook ✦ Building Production-Grade Web Applications with Supabase ✦ Python Data Cleaning and Preparation Best Practices ✦ Unlocking Data with Generative AI and RAG ✦ Principles of Data Science - Third Edition ✦ The Machine Learning Solutions Architect Handbook - Second Edition ✦ Causal Inference and Discovery in Python✦ Bayesian Analysis with Python - Third Edition ✦ Hands-On Genetic Algorithms with Python - Second Edition ✦ Learning Geospatial Analysis with Python - Fourth Edition ✦ Data Science with .NET and Polyglot Notebooks ✦ TradeStation EasyLanguage for Algorithmic TradingDon't miss this opportunity to upskill and elevate your career. Ready to dive in?➽ RAG-Driven Generative AI: This new title, RAG-Driven Generative AI, is perfect for engineers and database developers looking to build AI systems that give accurate, reliable answers by connecting responses to their source documents. It helps you reduce hallucinations, balance cost and performance, and improve accuracy using real-time feedback and tools like Pinecone and Deep Lake. By the end, you’ll know how to design AI that makes smart decisions based on real-world data—perfect for scaling projects and staying competitive! Start your free trial for access, renewing at $19.99/month.eBook $24.99 $35.99Print + eBook $29.99 $43.99➽ The AI Value Playbook: This new title, The AI Value Playbook, helps tech leaders understand how to harness AI to boost business value. It breaks down key AI concepts and tech in simple terms, shares real-world examples from business leaders, and offers strategies to integrate AI into your company. Perfect for navigating AI’s fast-paced changes and applying it effectively across various sectors! Start your free trial for access, renewing at $19.99/month.eBook $35.99Print + eBook $30.99 $44.99➽ Building Production-Grade Web Applications with Supabase: This new book is all about helping you master Supabase and Next.js to build scalable, secure web apps. It’s perfect for solving tech challenges like real-time data handling, file storage, and enhancing app security. You'll even learn how to automate tasks and work with multi-tenant systems, making your projects more efficient. By the end, you'll be a Supabase pro! Start your free trial for access, renewing at $19.99/month.eBook $15.99 $31.99Print + eBook $27.98 $39.99➽ Python Data Cleaning and Preparation Best Practices: This new book is a great guide for improving data quality and handling. It helps solve common tech issues like messy, incomplete data and missing out on insights from unstructured data. You’ll learn how to clean, validate, and transform both structured and unstructured data—think text, images, and audio—making your data pipelines reliable and your results more meaningful. Perfect for sharpening your data skills! Start your free trial for access, renewing at $19.99/month.eBook $24.99 $35.99Print + eBook $30.99 $44.99➽ Unlocking Data with Generative AI and RAG: This new book is all about helping tech teams solve real-world problems using retrieval-augmented generation (RAG). It simplifies how to combine large language models (LLMs) with your own data to improve AI apps, like search engines or chatbots. You'll learn to manage vector databases, automate workflows, and tackle common challenges in RAG implementation, making your AI systems smarter and more effective in decision-making. Start your free trial for access, renewing at $19.99/month.eBook $21.99 $31.99Print + eBook $27.98 $39.99➽ Principles of Data Science - Third Edition: This book is perfect for dataprofessionals who want to make sense of big data and build machine learning models that work. It helps solve problems like cleaning messy data, creating insightful visualizations, and managing biases in models. You’ll learn practical ways to deploy ML pipelines, handle complex datasets, and tackle real-world challenges with case studies. It's a clear guide for mastering data science, from math to machine learning! Start your free trial for access, renewing at $19.99/month.eBook $19.99 $39.99Print + eBook $34.98 $49.99➽ Bayesian Analysis with Python - Third Edition: This book is all about helping you tackle data analysis using Bayesian methods. It solves the challenge of building and interpreting complex models, making it easier with step-by-step guidance using tools like PyMC and ArviZ. You’ll learn how to create models, check them for accuracy, and apply Bayesian thinking to real-world problems—perfect for anyone wanting to strengthen their data science skills! Start your free trial for access, renewing at $19.99/month.eBook $19.99 $39.99Print + eBook $34.98 $49.99➽ The Machine Learning Solutions Architect Handbook - Second Edition: This book is for tech professionals who want to solve big machine learning challenges using cloud tools like AWS. It tackles issues like managing risks in the ML lifecycle, building scalable architectures, and understanding generative AI. You’ll learn how to design ML solutions that work in real-world business scenarios, from training large models to using generative AI effectively. By the end, you'll be ready to handle the technical and business sides of ML projects with confidence. Start your free trial for access, renewing at $19.99/month.eBook $15.99 $32.99Print + eBook $27.99 $40.99Eager for more insights? Add these powerful resources to your reading list.➽ Causal Inference and Discovery in Python:This book is all about helping you solve the tricky problems of understanding cause and effect in data.It teaches you how to go beyond traditional stats with causal methods, like figuring out what really drives outcomes using Python. You’ll learn key concepts like interventions and counterfactuals, and master tools for discovering causal relationships—useful for building smarter, more reliable models. Perfect for anyone looking to level up their data science game with causal thinking!➽ Hands-On Genetic Algorithms with Python - Second Edition: This book focuses on solving optimization challenges using genetic algorithms in Python. You'll discover how to tackle complex problems like planning, scheduling, and enhancing machine learning models. It covers bio-inspired algorithms like PSO and NEAT, leveraging cloud computing for faster solutions, and applying AI techniques such as NLP and reinforcement learning. It's a great guide for taking your AI projects to the next level with powerful, scalable solutions!➽ Learning Geospatial Analysis with Python - Fourth Edition: This book helps you create powerful GIS solutions using Python 3.10, guiding you through essential geospatial tools like PostGIS, QGIS, and PROJ. It covers automating workflows, handling various data formats, and creating stunning visualizations with Python libraries. You'll also explore remote sensing, real-time data tracking, and geospatial modeling for applications like urban planning and disaster management. Perfect for building customized geospatial apps and unlocking the full potential of geospatial technology!➽ Data Science with .NET and Polyglot Notebooks: This book is a hands-on guide for .NET developers looking to dive into data science, machine learning, and AI. It helps you conduct data experiments using Polyglot Notebooks and .NET Interactive tools, covering key topics like data analysis, model training, and AI orchestration. With interactive examples and online code access, you'll gain practical skills to solve real-world problems and apply advanced data science techniques in your .NET projects. Perfect for expanding your .NET expertise into the world of analytics and AI!➽ TradeStation EasyLanguage for Algorithmic Trading: This book is perfect for traders wanting to learn algorithmic trading using TradeStation EasyLanguage. It guides you through building tools for Equities, Futures, and Forex markets, combining traditional trading techniques with machine learning to refine your strategies. You'll also learn risk management algorithms and how to program for mechanical trading, giving you a well-rounded, hands-on approach to mastering algorithmic trading in the financial markets. Great for those looking to blend AI with technical trading!We’ve got more great things coming your way—see you soon! *{box-sizing:border-box}body{margin:0;padding:0}a[x-apple-data-detectors]{color:inherit!important;text-decoration:inherit!important}#MessageViewBody a{color:inherit;text-decoration:none}p{line-height:inherit}.desktop_hide,.desktop_hide table{mso-hide:all;display:none;max-height:0;overflow:hidden}.image_block img+div{display:none}sub,sup{line-height:0;font-size:75%} @media (max-width: 100%;display:block}.mobile_hide{min-height:0;max-height:0;max-width: 100%;overflow:hidden;font-size:0}.desktop_hide,.desktop_hide table{display:table!important;max-height:none!important}} @media only screen and (max-width: 100%;} #pad-desktop {display: none !important;} } @media only screen and (max-width: 100%;} #pad-desktop {display: none !important;} }

Podcast with Gemini 1.5 Pro, Structured Generation for LLM-as-a-Judge Evaluations, Arabic Stable LMStop worrying about your to-do list.Zapier connects the apps you use every day, so you can focus on what matters most.Start working more efficiently - Create your free account today.Get started for freeSponsored🗞️ Welcome to DataPro #124 – Your Weekly Data Science & ML Wizardry! 🌟Stay on top of the AI and ML game with cutting-edge tools, insights, and strategies. This week, we’re bringing you trending resources to supercharge your projects, enhance accuracy, and drive innovation. Let’s dive in!🔍 Algorithm Spotlight: Models Making Waves✦ Google Gemini 2.0: Ushering in the agentic AI era.✦ AlphaQubit: Google’s breakthrough in quantum error correction.✦ Genie 2: A massive foundation world model.✦ OpenAI’s GPT-4o-mini: Transforming retail experiences.✦ Microsoft's AI Carbon Tracker: Real-time global emission monitoring.✦ Quartz Atlas AI: Accelerating drug discovery.🚀 Trend Watch: What’s Hot in Tech✦ Top 5 Tips for Fine-Tuning LLMs.✦ AI Implementation Lessons from Early Adopters.✦ DeepSeek V2.5: Next-gen insights.✦ MAG-V by Splunk: AI innovation decoded.✦ Stability AI’s Arabic Stable LM 1.6B: A new language model frontier.🛠️ Tool Picks: ML Services in the Spotlight✦ 7 Python Libraries Every MLOps Pro Needs.✦ The Dark Side of Tech: Misuse in Education.✦ EXAONE 3.5 by LG AI Research: Advancing AI capabilities.✦ CePO by Cerebras: Smart planning and optimization.✦ Hugging Face TGI v3.0: Revolutionizing text generation.✦ Meta AI SPDL: Efficient data loading at scale.📊 ML in Action: Stories That Inspire✦ Gemini 1.5 Pro: Building a podcast powerhouse.✦ Text Classification 101 with Hugging Face Transformers.✦ 3 Key Business Skills for Data Science Careers in 2025.✦ LLM-as-a-Judge: Structured Generation in Practice.✦ Shopify Case Study: Using synthetic data effectively.✦ Combining Big and Small LLMs for Faster, Better Inference.✦ Building a Versatile LLM Agent: Step by Step.Enjoy exploring, learning, and building this week!Stay tuned and stay inspired – there’s always something new to discover in the ever-evolving world of Data Science and Machine Learning!Take our weekly survey and get a free PDF copy of our best-selling book,"Interactive Data Visualization with Python - Second Edition."We appreciate your input and hope you enjoy the book!Share Your Insights and Shine! 🌟💬This is our final edition of DataPro for 2024, but don’t worry—we’ll be back with more insights and updates in January 2025. In the meantime, we’ve got a little holiday treat for you!Packt has some exciting offers lined up to help you boost your tech skills and get ready for an amazing new year! It’s the perfect opportunity to relax, learn something new, and stay ahead in your field. Keep an eye out for these special holiday deals!From all of us at the Packt Newsletters team, we wish you a joyful holiday season and a fantastic start to 2025. See you next year! 🎄✨Cheers,Merlyn ShelleyEditor-in-Chief, Packt.Mastering Software Deployments at the Edge: A User’s Guide to Diverting DisasterSoftware delivery to dedicated edge devices is one of the most complex challenges faced by IT professionals today. While edge deployments come with inherent complications, it’s possible to avoid the pitfalls. With this guide in hand, a little planning, and the right tools and strategies in place, you can be confident you’ll never push a faulty update at scale.Read the GuideSponsored📚 Packt Signature Series: Must-Reads & Author Insights➽ RAG-Driven Generative AI: This new title, RAG-Driven Generative AI, is perfect for engineers and database developers looking to build AI systems that give accurate, reliable answers by connecting responses to their source documents. It helps you reduce hallucinations, balance cost and performance, and improve accuracy using real-time feedback and tools like Pinecone and Deep Lake. By the end, you’ll know how to design AI that makes smart decisions based on real-world data—perfect for scaling projects and staying competitive! Start your free trial for access, renewing at $19.99/month.eBook $24.99 $35.99Print + eBook $43.99➽ Building Production-Grade Web Applications with Supabase: This new book is all about helping you master Supabase and Next.js to build scalable, secure web apps. It’s perfect for solving tech challenges like real-time data handling, file storage, and enhancing app security. You'll even learn how to automate tasks and work with multi-tenant systems, making your projects more efficient. By the end, you'll be a Supabase pro! Start your free trial for access, renewing at $19.99/month.eBook $15.99 $31.99Print + eBook $39.99➽ Python Data Cleaning and Preparation Best Practices: This new book is a great guide for improving data quality and handling. It helps solve common tech issues like messy, incomplete data and missing out on insights from unstructured data. You’ll learn how to clean, validate, and transform both structured and unstructured data—think text, images, and audio—making your data pipelines reliable and your results more meaningful. Perfect for sharpening your data skills! Start your free trial for access, renewing at $19.99/month.eBook $24.99 $35.99Print + eBook $44.99🔍 Model Breakdown: Unveiling the Algorithm of the Week➽ Google introduces Gemini 2.0: A new AI model for the agentic era. Google has introduced Gemini 2.0, its most advanced AI model yet, with groundbreaking multimodal capabilities, agentic features for enhanced reasoning, and integration across products like Search. It’s faster, smarter, and redefines AI’s role as a universal assistant.➽ AlphaQubit: Google’s research on quantum error correction. Google DeepMind and Quantum AI introduce AlphaQubit, a groundbreaking AI decoder that improves quantum error correction with unmatched accuracy. This innovation brings us closer to reliable quantum computing, unlocking possibilities in drug discovery, material design, and fundamental science.➽ Genie 2: A large-scale foundation world model. Google DeepMind unveils Genie 2, a cutting-edge world model generating endless 3D environments for training AI and interactive gameplay. From a single image prompt, it creates action-controllable worlds, accelerating embodied agent development and advancing AI research.➽ Boosting the customer retail experience with GPT-4o-mini: Zalando, Europe’s leading online fashion platform, partnered with OpenAI to enhance its AI-powered Zalando Assistant. Upgraded to GPT-4o mini, the Assistant now delivers personalized recommendations in 25 markets, boosting product clicks by 23%, wishlists by 41%, and reducing costs.➽ Microsoft Research Introduces AI-Powered Carbon Budgeting Method: A Real-Time Approach to Tracking Global Carbon Sinks and Emission. Microsoft Research Asia, in collaboration with global institutions, introduces an AI-powered method for near-real-time carbon budgeting. Using satellite data and machine learning, the model predicts global carbon sinks with unprecedented speed and accuracy, addressing critical climate change challenges.➽ Quartz Atlas AI for Drug Discovery: Quartz Atlas AI™, developed by Deloitte and AWS, revolutionizes drug discovery by streamlining data connectivity, enhancing insights with domain-specific AI models, and simplifying accessibility for researchers. This AI-powered workbench accelerates R&D while reducing reliance on costly, unproductive trials.🚀 Trendspotting: What's Next in Tech Trends➽ Top 5 Tips for Fine-Tuning LLMs: Fine-tuning large language models (LLMs) can unlock domain-specific performance for tasks in medicine, law, and beyond. By prioritizing data quality and selecting the right architecture, like GPT for generation or BERT for comprehension, models become more robust and effective.➽ Overcoming AI Implementation Challenges: Lessons from Early Adopters. Implementing AI is transformative but challenging, with hurdles like data quality, accessibility, and talent shortages. Early adopters share valuable lessons in overcoming these issues, emphasizing robust data management, scalable infrastructure, and fostering skilled talent for successful AI adoption.➽ DeepSeek AI Just Released DeepSeek-V2.5-1210: DeepSeek AI introduces DeepSeek-V2.5-1210, an enhanced model excelling in mathematics, coding, writing, and reasoning. With improved accuracy, live coding capabilities, and user-friendly features, it’s a versatile tool for researchers, developers, and professionals across diverse fields.➽ Splunk Researchers Introduce MAG-V: Splunk Inc. introduces MAG-V, a multi-agent framework addressing challenges in AI trajectory verification and synthetic data generation. By combining machine learning and deterministic methods, MAG-V ensures accuracy, scalability, and privacy while outperforming traditional LLM-based solutions in reliability and cost-efficiency.➽ Stability AI Releases Arabic Stable LM 1.6B Base and Chat Models: Stability AI's Arabic Stable LM 1.6B offers a resource-efficient solution for Arabic NLP, balancing cultural alignment and performance. With fine-tuning on over 100 billion tokens, it excels in tasks like question answering and cultural context recognition, advancing inclusivity in language AI.🛠️ Platform Showdown: Comparing ML Tools & Services➽ 7 Essential Python Libraries for MLOps: This blog explores seven essential Python libraries for MLOps, enabling users to streamline machine learning workflows, from experiment tracking and orchestration to model serving and performance monitoring, with tools like MLflow and Prefect.➽ Accusatory AI: How misuse of technology is harming students. This blog discusses the flaws of AI-powered cheating detection tools in education, highlighting their potential for false accusations against students. It emphasizes the importance of transparency, evidence, and fairness, urging educators to use these tools constructively rather than as punitive measures.➽ LG AI Research Releases EXAONE 3.5: LG AI Research's EXAONE 3.5 introduces advanced bilingual models excelling in English and Korean tasks, offering long-context processing, scalability, and cost-efficiency. With three versions optimized for diverse applications, EXAONE 3.5 sets new benchmarks in language AI performance.➽ Cerebras Introduces CePO (Cerebras Planning and Optimization): Cerebras introduces CePO, an AI framework enhancing Llama models with embedded planning and reasoning capabilities. CePO streamlines complex decision-making in industries like logistics and healthcare, combining neural-symbolic methods for adaptability, efficiency, and scalability in advanced optimization tasks.➽ Hugging Face Releases Text Generation Inference (TGI) v3.0: Hugging Face's Text Generation Inference (TGI) v3.0 enhances text generation efficiency, offering 13x faster processing, 3x higher token capacity, and reduced memory usage. It simplifies deployment with zero-configuration, enabling scalable, high-performance NLP for long prompts and dynamic contexts.➽ Meta AI Introduces SPDL (Scalable and Performant Data Loading): Meta AI's SPDL (Scalable and Performant Data Loading) optimizes AI training by accelerating data delivery to GPUs. With thread-based architecture, prefetching, and caching, SPDL reduces training times, cuts costs, and boosts efficiency, making it ideal for large-scale, distributed AI workflows.📊 Success Stories: Real-World ML Case Studies➽ Learn how to build a podcast with Gemini 1.5 Pro: Google Cloud's Gemini 1.5 Pro and Text-to-Speech API enable creators to generate custom podcasts by transforming written content into engaging audio formats. With diverse voices, multilingual support, and script generation, this approach expands reach, boosts engagement, and repurposes content effortlessly.➽ How to Build a Text Classification Model with Hugging Face Transformers? This article explains how to train a transformer-based text classification model using Hugging Face Transformers in five simple steps. It covers loading data, tokenizing, initializing model architecture, and fine-tuning with ease for custom tasks.➽ 3 Business Skills You Need to Progress Your Data Science Career in 2025: This blog highlights the essential business and strategic skills data scientists need as they transition into leadership roles. It emphasizes the importance of financial fluency, staying updated on AI/ML trends, and aligning technical expertise with business impact for career growth.➽ How to Use Structured Generation for LLM-as-a-Judge Evaluations? This blog explores the concept of structured generation, a method to guide large language model (LLM) outputs into specific formats using schemas like context-free grammars (CFG). It demonstrates how structured generation enhances tasks such as hallucination detection and content validation in LLM-based evaluations.➽ Synthetic Data in Practice: A Shopify Case Study: This blog examines the practical utility of synthetic data through a side-by-side comparison of 30,000 real Shopify transactions and their synthetic counterparts. It evaluates how closely synthetic data mirrors real trends, identifies discrepancies, and highlights when it’s reliable for decision-making.➽ Combining Large and Small LLMs to Boost Inference Time and Quality: This blog explores efficient and high-quality text generation strategies using contrastive decoding, combining large and small language models. It demonstrates how optimizing token selection improves inference speed and output reliability in large language models like GPT-2.➽ How to Build a General-Purpose LLM Agent? This blog explains how to build a general-purpose LLM agent, a versatile system capable of executing user queries with adaptable workflows. It covers selecting the right LLM, defining agent control logic, and leveraging agentic architectures for diverse, flexible use cases.We’ve got more great things coming your way—see you soon!*{box-sizing:border-box}body{margin:0;padding:0}a[x-apple-data-detectors]{color:inherit!important;text-decoration:inherit!important}#MessageViewBody a{color:inherit;text-decoration:none}p{line-height:inherit}.desktop_hide,.desktop_hide table{mso-hide:all;display:none;max-height:0;overflow:hidden}.image_block img+div{display:none}sub,sup{font-size:75%;line-height:0} @media (max-width: 100%;display:block}.mobile_hide{min-height:0;max-height:0;max-width: 100%;overflow:hidden;font-size:0}.desktop_hide,.desktop_hide table{display:table!important;max-height:none!important}}

DeepSeek-AI’s Fire-Flyer AI-HPC, Microsoft’s Brain-Inspired AI Design, Fairness in Graph Filtering👋 Hello ,Happy Friday! 🌟Welcome to DataPro #109—Your Weekly Data Science & ML Digest! 🚀This week’s edition is packed with exciting updates! Discover Table-Augmented Generation (TAG) for smarter querying, Vectorlite v0.2.0 for speedy SQL-powered search, Zyphra's Zamba2-mini, and Weaviate's StructuredRAG for reliable AI outputs. Plus, we’ve curated top resources to supercharge your ML models with enhanced accuracy and efficiency!⚡ Tech Tidbits: Fresh Innovations and Tools▪️ AWS: Speed up AI inference with NVIDIA NIM on SageMaker and integrate Amazon Q with GitHub.▪️ Google ML: Explore multimodal search with BigQuery and get the lowdown on Imagen 3 on Vertex AI.▪️ Microsoft Research: Dive into brain-inspired AI design for next-gen tech.📚 Hot Reads from Packt Library▪️ Data Science Fundamentals Pocket Primer: Your essential guide to data science concepts.▪️ Mastering Looker and LookML: Create insightful views, dashboards, and databases.▪️ AI and Expert Systems: Techniques and applications for solving real-world problems.🔍 From Bits to BERT: LLMs & GPTs Spotlight▪️ TAG: Revolutionize database querying with a unified approach.▪️ Vectorlite v0.2.0: Get SQL-powered vector search with speed.▪️ StructuredRAG by Weaviate: Benchmark for reliable JSON outputs in AI.▪️ Cerebras DocChat: Fast, Llama 3-based GPT-4-level QA.▪️ Extension|OS: Open-source tool for on-demand AI access.▪️ AI21 Labs' Jamba 1.5: Quick, high-quality multilingual AI.▪️ LayerPano3D: AI framework for generating 3D scenes from text.▪️ Zyphra's Zamba2-mini: High-performance small language model.▪️ Fairness in Graph Filtering: Framework for better AI fairness.▪️ iAsk AI: Outperforming ChatGPT on MMLU Pro Test.▪️ DeepSeek-AI’s Fire-Flyer AI-HPC: Cost-effective deep learning solution.✨ On the Radar: What’s New & Noteworthy▪️ New LLM Agents: Exploring the latest architecture.▪️ Pandas Power: Advanced plotting techniques.▪️ AWS DeepRacer: Bridging the Sim2Real gap.▪️ MarianMT Translation: Easy language translation with Hugging Face Transformers.▪️ Building Transformers: A guide to training from scratch.▪️ ML Optimization: Top tips for boosting algorithm performance.Enjoy your weekend and stay ahead in the world of data science!DataPro Newsletter is not just a publication; it’s a complete toolkit for anyone serious about mastering the ever-changing landscape of data and AI. Grab your copyand start transforming your data expertise today!Calling Data & ML Enthusiasts!Want to share your insights and build your online reputation? Contribute to our new Packt DataPro column! Discuss tools, share experiences, or ask questions. Gain recognition among 128,000+ data professionals and boost your CV. Simply reply with your Google Docs link or use our feedback form. Whether you’re looking for visibility or a discreet approach, we’re here to support you.Share your content today and engage with our vibrant community! We’re excited to hear from you!Take our weekly survey and get a free PDF copy of our best-selling book,"Interactive Data Visualization with Python - Second Edition."We appreciate your input and hope you enjoy the book!Share Your Insights and Shine! 💬📚Expert Insights from Packt CommunityDid you know? “Books are the quietest, most constant friends, holding the world’s treasured wisdom. They offer gentle guidance and timeless lessons, passing their rich inheritance from one generation to the next.”We’re thrilled to bring you this week’s must-have new releases, straight from the experts to your bookshelf! Whether you're eager to enhance your skills or explore new horizons, now is the perfect moment to add these invaluable resources to your collection.For a limited time, enjoy 30% off all eBooks at Packtpub.com. These books are thoughtfully crafted by industry insiders with hands-on experience, offering unique insights you won’t find anywhere else.Don’t let these Packt-exclusive deals slip away—seize the opportunity to learn from the best at an unbeatable price!Order Today at $41.98 $59.99Data Science Fundamentals Pocket Primer: An Essential Guide to Data Science Concepts and TechniquesBy Mercury Learning and Information, Oswald CampesatoImagine having a go-to guide that gently walks you through the essentials of data science, making complex concepts feel accessible. This book does just that. With a blend of practical exercises and real-world examples, it simplifies the vast world of data science. Here’s what you’ll love:- A clear introduction to data science fundamentals.- Hands-on learning with practical examples.- Mastery of tools like Python, NumPy, Pandas, and R.- Techniques for data visualization to bring your data to life.Whether you're just starting or looking to sharpen your skills, this book is your companion on the journey to mastering data science.Get your copy now for $41.98 (originally $59.99).Order TodayMastering Looker and LookML - Complete Looker Guide for Developers: Master Looker and LookML to create views, dashboards, and databases with this guide [Video]By HHN Automate Book Inc.Embark on a journey to unlock the full potential of Looker with our all-encompassing course. Whether you’re new to Looker or looking to deepen your skills, this course guides you step-by-step through everything you need to know.Here’s what you can expect:- Hands-on tutorials for setting up your environment and connecting data.- In-depth exploration of LookML fields, parameters, and joins.- Advanced techniques for creating and managing impactful dashboards.By the end, you’ll have the confidence to create dynamic, data-driven insights that can drive meaningful decisions in your organization.Get the full video course now for $104.99 (MP4 download available).Order Today at $34.98 $49.99Artificial Intelligence and Expert Systems: Techniques and Applications for Problem SolvingBy Mercury Learning and Information ,I. Gupta ,G. NagpalDive into the world of AI with a guide that makes complex concepts approachable and practical. This book is your gateway to mastering AI, offering:- In-depth coverage of AI and expert systems.- Clear explanations paired with real-world applications.- Exploration of advanced topics like neural networks and fuzzy logic.From understanding the basics of AI to applying expert systems and neural networks, this book equips you with the tools to solve real-world problems. Perfect for anyone eager to enhance their knowledge of intelligent systems.Grab your copy now for $34.98 (originally $49.99).🔰 Data Science Tool Kit➤ NicolasHug/Surprise:Python scikit for building recommender systems with explicit rating data, emphasizing experiment control, dataset handling, and diverse prediction algorithms.➤ gorse-io/gorse:Open-source recommendation system in Go, designed for universal integration into online services, automating model training based on user interaction data.➤ recommenders-team/recommenders:Recommenders, a Linux Foundation project, offers Jupyter notebooks for building classic and cutting-edge recommendation systems, covering data prep, modeling, evaluation, optimization, and production deployment on Azure.➤ alibaba/Alink:Alink, developed by Alibaba's PAI team, integrates Flink for ML algorithms. PyAlink supports various Flink versions, maintaining compatibility up to Flink 1.13.➤ RUCAIBox/RecBole:RecBole, built on Python and PyTorch, facilitates research with 91 recommendation algorithms across general, sequential, context-aware, and knowledge-based categories.Access 100+ data tools in this specially curated blog, covering everything from data analytics to business intelligence—all in one place. Check out"Top 100+ Essential Data Science Tools & Repos: Streamline Your Workflow Today!"on PacktPub.com.⚡Tech Tidbits: Stay Wired to the Latest Industry Buzz!AWS ML Made Easy➤ Accelerate Generative AI Inference with NVIDIA NIM Microservices on Amazon SageMaker: The blog details NVIDIA's new NIM Inference Microservices integration with Amazon SageMaker, enabling fast, cost-effective deployment of large language models. It covers the use of prebuilt containers for efficient AI inferencing and provides a guide for setup and evaluation.➤ Connect the Amazon Q Business generative AI coding companion to your GitHub repositories with Amazon Q GitHub (Cloud) connector: This blog explains how incorporating generative AI, like Amazon Q Developer, can boost development productivity by up to 30% and streamline developer tasks. It details integrating Amazon Q Business with GitHub (Cloud) for natural language queries to manage repositories and enhance enterprise operations.Mastering ML with Google➤ Multimodel search using NLP, BigQuery and embeddings: This blog introduces a new era in search with multimodal embeddings, enabling text-based queries for images and videos. It showcases a demo for cross-modal search using Google Cloud Storage and BigQuery, allowing users to search for visual content through text queries.➤ A developer's guide to Imagen 3 on Vertex AI: The blog highlights user feedback on Imagen 3, emphasizing its need for high-quality, versatile image generation. It discusses improvements in artistic style, prompt adherence, and safety features like watermarking. Code examples illustrate creating photorealistic images and rendering text with the model.Microsoft Research Insights➤ Innovations in AI: Brain-inspired design for more capable and sustainable technology. Microsoft Research Asia, in collaboration with multiple institutions, is developing brain-inspired AI models to improve efficiency and sustainability. Key projects include CircuitNet for neural patterns, enhanced spiking neural networks (SNNs) for time-series prediction, and integrating central pattern generators for better sequence processing.🔍From Bits to BERT: Keeping Up with LLMs & GPTs➤ Table-Augmented Generation (TAG): A Unified Method for Improved Database Querying. Researchers from UC Berkeley and Stanford propose Table-Augmented Generation (TAG) to improve natural language queries over databases. TAG enhances query handling by combining query synthesis, execution, and answer generation, outperforming existing methods like Text2SQL and RAG in accuracy and complexity.➤ Vectorlite v0.2.0: Fast, SQL-Powered Vector Search with SQLite Driver. Vectorlite v0.2.0 enhances performance by using Google’s highway library for vector distance, addressing hnswlib’s limitations on SIMD instruction support and vector normalization. The update improves speed significantly, especially on x64 platforms with AVX2, and is now SIMD-accelerated on ARM.➤ StructuredRAG by Weaviate: Benchmark for Reliable JSON Output in AI. The StructuredRAG benchmark evaluates LLMs' ability to generate structured outputs like JSON. Testing Gemini 1.5 Pro and Llama 3 8B-instruct with various prompting strategies revealed an 82.55% success rate on average, with performance varying significantly by task and model.➤ Cerebras DocChat: Llama 3-Based GPT-4-Level QA in Hours. Cerebras has released two models for document-based Q&A: Llama3-DocChat and Dragon-DocChat, trained quickly using Cerebras Systems. Llama3-DocChat builds on Llama 3, while Dragon-DocChat improves on Dragon+ with enhanced recall. Both models and their training data are open-source.➤ Extension|OS: Open-Source Browser Tool for On-Demand AI Access. Extension|OS is a browser extension that integrates AI tools directly into web pages, allowing users to perform tasks like grammar checks and content edits without switching tabs. It features prompt customization, secure API key storage, and enhanced functionality with a Mixture of Agents.➤ AI21 Labs' Jamba 1.5 Models: Speedy, Quality, Multilingual AI. AI21's Jamba 1.5 Open Model Family features the Jamba 1.5 Mini and Large models, built on the SSM-Transformer architecture. They offer the longest context window, exceptional speed, and high quality. Jamba 1.5 models outperform competitors and support extensive enterprise applications.➤ LayerPano3D: AI Framework for Consistent 3D Scene Generation from Text. LayerPano3D introduces a novel framework for generating full-view, explorable panoramic 3D scenes from a single text prompt. By decomposing 2D panoramas into layered 3D representations, it achieves high-quality, consistent views and immersive exploration, surpassing existing methods.➤ Zyphra's Zamba2-mini: Efficient, High-Performance Small Language Model. Zamba2-1.2B improves hybrid SSM-transformer models by adding rotary embeddings and LoRA projectors for depth-specialization, enhancing performance. Developed to optimize model efficiency and accuracy, it’s applicable in real-world scenarios like advanced NLP tasks and code generation.➤ Fairness in Graph Filtering: Framework for Theory and Mitigation Techniques. The paper addresses fairness in GNN-based recommendation systems, which often overlook consumer fairness. It evaluates a new method for adjusting fairness via fair graph augmentation. This approach consistently improves fairness across various GNN models and datasets, advancing recommendation system equity.➤ iAsk Ai Outperforms ChatGPT and Others on MMLU Pro Test: The iAsk Pro model achieved a record 85.85% accuracy on the MMLU-Pro benchmark, surpassing all current LLMs, including GPT-4o, by over 13 percentage points. This dataset, with 12,000 complex questions, tests multi-task language comprehension rigorously. iAsk Pro's performance highlights its advanced reasoning and understanding capabilities, setting a new standard in AI evaluation.➤ Lite Oute 2 Mamba2Attn 250M: 10X More Efficient AI. The Lite Oute 2 Mamba2Attn 250M model, using the new Mamba2 architecture with attention layers, boasts 250 million parameters and achieves high benchmark scores. It was developed for improved efficiency and performance in various tasks, showing enhanced results in multiple evaluations compared to previous models.➤ DeepSeek-AI Launches Fire-Flyer AI-HPC: Cost-Effective Deep Learning Solution. The Fire-Flyer AI-HPC architecture addresses high costs and energy demands in Deep Learning by integrating hardware-software design. With 10,000 PCIe A100 GPUs, it cuts costs by 50% and reduces energy use by 40%, improving scalability and performance.✨On the Radar: Catch Up on What's Fresh➤ Navigating the New Types of LLM Agents and Architectures: The post explores the evolution of AI agents from early ReAct models to the second generation of more structured, efficient agents. It introduces tools and frameworks for building these agents and highlights advancements in design and performance. Key insights include improvements in routing and state management.➤ The Power of Pandas Plots: Backends. The article highlights how Pandas can leverage various visualization backends, such as Matplotlib, Plotly, and Hvplot, to enhance data visualization without extensive retraining. It shows how easy it is to switch between these backends for interactive and efficient plotting, emphasizing Hvplot's ease of use and integration.➤ AWS DeepRacer : A Practical Guide to Reducing The Sim2Real Gap. The article focuses on training the AWS DeepRacer to safely navigate a track. It emphasizes creating a "safe" model that prioritizes staying on the track over speed. Key aspects include setting up the track, designing reward functions, and using a discrete action space. It details iterative training, starting with slower models and gradually increasing speed, to enhance both safety and performance. The final reward function balances staying on the track and adjusting speed for turns, with iterative improvements for increased reliability.➤ How to Translate Languages with MarianMT and Hugging Face Transformers? The article explains how to use MarianMT with Hugging Face Transformers for language translation. It covers installation, model selection, loading, tokenization, and translating text. The guide provides steps for translating to multiple languages and highlights MarianMT’s ease of use and effectiveness.➤ How to Build and Train a Transformer Model from Scratch with Hugging Face Transformers? The Hugging Face Transformers library enables both the use of pre-trained models and the creation of custom transformer models from scratch. This tutorial guides you through setting up, tokenizing data, configuring, and training a transformer for sentiment classification, emphasizing the need for high-performance computing resources.➤ 5 Tips for Optimizing Machine Learning Algorithms: This blog provides key tips for optimizing machine learning algorithms, focusing on data preparation, hyperparameter tuning, cross-validation, regularization, and ensemble methods. It aims to improve the accuracy, efficiency, and robustness of ML models for real-world applications.See you next time!*{box-sizing:border-box}body{margin:0;padding:0}a[x-apple-data-detectors]{color:inherit!important;text-decoration:inherit!important}#MessageViewBody a{color:inherit;text-decoration:none}p{line-height:inherit}.desktop_hide,.desktop_hide table{mso-hide:all;display:none;max-height:0;overflow:hidden}.image_block img+div{display:none}sub,sup{line-height:0;font-size:75%} @media (max-width: 100%;display:block}.mobile_hide{min-height:0;max-height:0;max-width: 100%;overflow:hidden;font-size:0}.desktop_hide,.desktop_hide table{display:table!important;max-height:none!important}}

For a limited time, save on the best-selling books that will elevate your skills and knowledge! @media only screen and (max-width: 100%;} #pad-desktop {display: none !important;} }👋 Hello ,✨ Welcome to Packt’s Signature Series: New Titles Just Arrived!📚 We’re excited to present a new collection in our Signature Series, featuring the best-selling titles in the data industry. Packed with insights on Generative AI and multimodal systems, this collection is available for a limited time at 30% off both print and e-book formats. This offer ends Sunday, September 22nd. Don’t miss your chance to upskill and elevate your career. Let’s dive in!➽ Building LLM Powered Applications: This new titleis all about helping engineers and data pros use large language models (LLMs) effectively. It tackles key challenges like embedding LLMs into real-world apps and mastering prompt engineering techniques. You’ll learn to orchestrate LLMs with LangChain and explore various models, making it easier to create intelligent systems that can handle both structured and unstructured data. It’s a great way to boost your skills, whether you’re new to AI or already experienced! Start your free trial for access, renewing at $19.99/month.eBook $27.98 $39.99Print + eBook $34.98 $49.99➽ Python for Algorithmic Trading Cookbook: This bookis your go-to guide for using Python in trading. It helps you tackle key issues like acquiring and visualizing market data, designing and backtesting trading strategies, and deploying them live with APIs. You’ll learn practical techniques to gather data, analyze it, and optimize your strategies using tools like OpenBB and VectorBT. Whether you’re just starting or looking to refine your skills, this book equips you with the know-how to trade smarter with Python! Start your free trial for access, renewing at $19.99/month.eBook $27.98 $39.99Print + eBook $36.99 $49.99➽ Microsoft Power BI Cookbook - Third Edition: The Power BI Cookbook is your essential guide to mastering data analysis and visualization with Power BI. It covers using Microsoft Data Fabric, managing Hybrid tables, and creating effective scorecards. Learn to transform complex data into clear visuals, implement robust models, and enhance reports with real-time data. This updated edition prepares you for future AI innovations, making it a must-have for beginners and seasoned users alike! Start your free trial for access, renewing at $19.99/month.eBook $29.99 $43.99Print + eBook $41.98 $59.99➽ The Definitive Guide to Power Query (M): The Definitive Guide to Power Query (M) focuses on mastering data transformation with Power Query. It covers fundamental and advanced concepts through hands-on examples that address real-world problems. You'll learn the Power Query M language, optimize performance, handle errors, and implement efficient data processes. By the end, you'll have the skills to enhance your data analysis effectively! Start your free trial for access, renewing at $19.99/month.eBook $43.99Print + eBook $37.99 $54.99➽ Mastering PyTorch - Second Edition: This is your essential resource for building advanced neural network models with PyTorch. You'll explore tools like Hugging Face, fastai, and Docker, learning to create models for text, images, and music. With hands-on examples, you'll master training optimization, mobile deployment, and various network types, equipping you to tackle complex AI tasks using the PyTorch ecosystem! Start your free trial for access, renewing at $19.99/month.eBook $28.99 $41.99Print + eBook $40.99 $51.99➽ Unlocking the Secrets of Prompt Engineering: It'syour guide to mastering AI-driven writing with large language models (LLMs). It covers essential techniques and applications, from content creation to chatbots. With practical examples, you'll learn to generate product descriptions and tackle advanced uses like podcast creation. The book emphasizes ethical practices and optimization strategies, preparing you to leverage AI for improved writing, creativity, and productivity! Start your free trial for access, renewing at $19.99/month.eBook $24.99 $35.99Print + eBook $30.99 $44.99➽ ChatGPT for Cybersecurity Cookbook: Your essential guide to using AI in cybersecurity. It helps you automate tasks like penetration testing, risk assessment, and threat detection with ChatGPT. Each recipe provides step-by-step instructions for generating commands, writing code, and creating tools with the OpenAI API and Python. You'll explore innovative strategies and optimize workflows, gaining confidence in AI-driven techniques to excel in the rapidly evolving cybersecurity landscape! Start your free trial for access, renewing at $19.99/month.eBook $27.98 $39.99Print + eBook $34.98 $49.99➽ Mastering NLP from Foundations to LLMs:Your complete guide to Natural Language Processing (NLP) with Python. It covers the mathematical foundations of machine learning and essential topics like linear algebra and statistics. You'll learn to preprocess text, classify it, and implement advanced techniques, including large language models (LLMs). With practical Python code samples and insights into future trends, you'll gain the skills to tackle real-world NLP challenges confidently and effectively design ML-NLP systems! Start your free trial for access, renewing at $19.99/month.eBook $29.99 $42.99Print + eBook $46.99 $52.99➽ Learn Microsoft Fabric: This title is your essential guide to using Microsoft Fabric for data integration and analytics. It explores key features with real-world examples, helping you build solutions for lakehouses, data warehouses, and real-time analytics. You'll learn to effectively monitor your Fabric platform and cover workloads like Data Factory and Power BI. By the end, you'll be equipped to unlock AI-driven insights and navigate the analytics landscape confidently! Start your free trial for access, renewing at $19.99/month.eBook $24.99 $35.99Print + eBook $35.98 $44.99➽ Building Data-Driven Applications with LlamaIndex: This book is your comprehensive guide to leveraging Generative AI and large language models (LLMs). It addresses challenges like memory constraints and data gaps while teaching you to build interactive applications with LlamaIndex. You'll learn to ingest and index data, create optimized indexes, and query your knowledge base through hands-on projects. By the end, you'll be equipped to troubleshoot LLM issues and confidently deploy your AI-driven applications! Start your free trial for access, renewing at $19.99/month.eBook $24.99 $35.99Print + eBook $30.99 $44.99➽ OpenAI API Cookbook: This new title is all about using the OpenAI API to create smart applications. It helps engineers and data pros understand the basics, set up their API, and build tailored tools like chatbots and virtual assistants. You’ll learn practical recipes to enhance user experience and integrate AI into your workflows, making your projects more efficient and innovative! Start your free trial for access, renewing at $19.99/month.eBook $21.99 $31.99Print + eBook $27.98 $39.99Loved Those Titles? Check These Out!➽ Data Governance Handbook➽ Generative AI for Cloud Solutions➽ Data-Centric Machine Learning with Python➽ Modern Python Cookbook - Third EditionWe’ve got more great things coming your way—see you soon!*{box-sizing:border-box}body{margin:0;padding:0}a[x-apple-data-detectors]{color:inherit!important;text-decoration:inherit!important}#MessageViewBody a{color:inherit;text-decoration:none}p{line-height:inherit}.desktop_hide,.desktop_hide table{mso-hide:all;display:none;max-height:0;overflow:hidden}.image_block img+div{display:none}sub,sup{line-height:0;font-size:75%} @media (max-width: 100%;display:block}.mobile_hide{min-height:0;max-height:0;max-width: 100%;overflow:hidden;font-size:0}.desktop_hide,.desktop_hide table{display:table!important;max-height:none!important}} @media only screen and (max-width: 100%;} #pad-desktop {display: none !important;} }

Google’s AI detective, Regnology Automates Ticket-to-Code with agentic GenAI on Vertex AI, MedFuzz @media only screen and (max-width: 100%;} #pad-desktop {display: none !important;} }Grow your business & career by 10x using AI Strategies in 4 hrs! 🤯Join GrowthSchool's AI Business Growth & Strategy Crash Course and discover how to revolutionise your approach to business on 12th September at 10 AM EST.In just 4 hours, you’ll gain the tools, insights, and strategies to not just survive, but dominate your market.This is more than just a workshop—it's a turning point.The first 100 to register get in for FREE. Don’t miss the chance to change your business trajectory forever.Sign up here to save your seat! 👈SponsoredWelcome to DataPro #111—Your Weekly Dose of Data Science & ML Magic! 🚀We’re now landing in your inbox every Thursday to keep you sharp and ahead of the game!In the ever-evolving realm of AI and ML, it's all about harnessing smart insights for impactful decisions and stellar leadership. Dive into our new Packt Signature Series, where you'll find expert tips on everything from real-time data management to mastering AI modeling. We’re here to equip you with the tools you need to navigate the data world like a pro.This week, we’ve got cutting-edge strategies to boost your model accuracy, optimize performance, and reduce costs with scalable solutions. Get ready for top-notch tips and practical techniques to supercharge your data skills.📚 Top Reads & Author Insights:✦ Building AI Intensive Python Applications:Dive deep into advanced AI apps.✦ Databricks ML in Action: Real-world applications and best practices.✦ Generative AI Application Integration Patterns:Innovative uses of generative AI.✦ Polars Cookbook:Essential recipes for efficient data handling.✦ Building LLM Powered Applications:Building with large language models.✦ Building Data-Driven Applications with LlamaIndex:Leveraging LlamaIndex for robust applications.✦ Data Quality in the Age of AI:Ensuring top-notch data quality.✦ Modern Computer Vision with PyTorch - Second Edition:Updated techniques in computer vision.✦ Accelerate Model Training with PyTorch 2.X:Speed up your model training.✦ Mastering PyTorch - Second Edition:The ultimate guide to mastering PyTorch.🔍 Algorithm Spotlight:✦ Apple’s Byte-Level ASR Optimization: A new AI algorithm for speech recognition.✦ IBM’s PowerLM-3B & PowerMoE-3B: Massive language models with advanced scheduling.✦ AtScale’s Open-Sourced SML: Transforming analytics with a new semantic modeling framework.✦ LG’s EXAONEPath: Enhancing histopathology analysis with a pre-trained model.🚀 Tech Trendwatch:✦ Tracing Memory Allocation in Python: Learn how to track memory usage.✦ Anomaly Detection in Streaming Data: Using Amazon Managed Service for Apache Flink.🛠️ ML Tool Showdown:✦7 Free Cloud IDEs You Need: Explore top IDEs for data science.✦ End-to-End Data Science Pipelines: From ingestion to visualization.✦ Sustainable MLOps: Optimizing operations for sustainability.📊 Success Stories:✦ GraphRAG’s Auto-Tuning: Adapting rapidly to new domains.✦ Enterprise Data Quality Guide: Navigating enterprise data challenges.✦ AI Agents for Daily Tasks: Automating routine app tasks.🌍 ML Newsflash:✦ Google’s AI Detective: Solving challenges with Gemini 1.5 Pro.✦ Regnology’s Gen AI on Vertex AI: Automating ticket-to-code processes.✦ MedFuzz on LLM Robustness: Evaluating LLMs in medical contexts.Stay tuned for your weekly dose of data brilliance! 🚀Take our weekly survey and get a free PDF copy of our best-selling book, "Interactive Data Visualization with Python - Second Edition."We appreciate your input and hope you enjoy the book!Share Your Insights and Shine! 🌟💬📚 Packt Signature Series: Must-Reads & Author InsightsStep into a world of expert-driven knowledge with ourone-of-a-kindin-house content, crafted by industry pros to deliver the freshest insights on the latest tech releases. Discover how these cutting-edge titles are shaping the data landscape and unlocking the "whats," "hows," and "whys" behind emerging technologies. Whether you're looking to sharpen your skills or dive into something entirely new, there's never been a better time to expand your library with these essential resources.For a limited time, enjoy 30% off all eBooks at Packtpub.com. These books are more than just guides, they’re packed with real-world expertise from those who know the industry inside and out, offering perspectives you simply won’t find anywhere else.➽ Building AI Intensive Python ApplicationsThis book guides you through building powerful AI applications using large language models (LLMs), vector databases, and Python frameworks. You'll learn how to optimize AI performance, implement advanced techniques like retrieval-augmented generation, and tackle challenges like hallucinations and data leakage, ultimately creating reliable, high-impact AI solutions.Order Today at $41.98 $59.99➽ Databricks ML in ActionThis book is all about mastering the Databricks platform for machine learning and data science. It helps data engineers and scientists solve key problems by offering practical, cloud-agnostic examples and code projects. You’ll learn how to use Databricks tools to streamline workflows, improve model performance, and integrate with third-party apps.Order Today at $24.99 $35.99➽ Generative AI Application Integration PatternsThis book guides you through designing and integrating GenAI applications. You’ll learn essential tools and strategies, from prompt engineering to advanced techniques like retrieval-augmented generation. It provides practical examples, a clear 4-step framework, and covers ethical considerations for deploying GenAI models effectively.Order Today at $27.98 $39.99➽ Polars CookbookThis cookbook is your go-to guide for mastering Python Polars, a high-performance library for efficient data analysis. It offers step-by-step recipes for handling large datasets, advanced querying, and performance optimization. With practical tips on data manipulation, integration, and deployment, you'll boost your data workflows and analysis skills.Order Today at $24.99 $35.99➽ Building LLM Powered ApplicationsThis book helps you integrate LLMs into real-world apps using LangChain for orchestration. It covers the basics and advanced techniques of prompt engineering, explores various LLM architectures, and guides you through using powerful tools to create intelligent agents. You'll also learn about ethical considerations and the future of large foundation models.Order Today at $27.98 $39.99➽ Building Data-Driven Applications with LlamaIndexThis guide explores Generative AI and LlamaIndex, focusing on overcoming LLM limitations and building interactive applications. Learn to manage text chunking, security, and real-time data challenges. With hands-on projects, you'll master data ingestion, indexing, querying, and deployment, equipping you to develop and customize sophisticated AI-driven solutions.Order Today at $24.99 $35.99➽ Data Quality in the Age of AIThis book emphasizes the crucial role of data quality in AI success. It provides strategies to improve and measure data quality, offering practical steps to enhance data-driven decision-making. With real-world examples and actionable insights, it equips teams to optimize their data culture, leading to better AI performance and business outcomes.Order Today at $55.98 $79.99➽ Modern Computer Vision with PyTorch - Second EditionThis book offers a deep dive into neural network architectures and PyTorch for computer vision tasks. Learn to build solutions for image classification, object detection, and more using state-of-the-art models like CLIP and Stable Diffusion. With code available on GitHub and Google Colab, you'll gain practical skills for real-world applications and production deployment.Order Today at $33.99 $48.99➽ Accelerate Model Training with PyTorch 2.XThis book helps you optimize PyTorch model training, focusing on reducing build time and improving efficiency. Learn to speed up training with multicore systems, multi-GPU setups, and mixed precision. You'll explore techniques for model simplification, specialized libraries, and data pipeline improvements to enhance performance and model quality.Order Today at $24.99 $35.99➽ Mastering PyTorch - Second Edition This book guides you through building advanced neural network models with PyTorch, including CNNs, RNNs, and transformers. Learn to optimize training with GPUs, deploy models on mobile, and utilize libraries like Hugging Face and PyTorch Lightning. It covers deep learning across text, vision, and music, enhancing your AI skills with practical techniques.Order Today at $28.99 $41.99🔍 Model Breakdown: Unveiling the Algorithm of the Week➽ Apple Researchers Propose a Novel AI Algorithm to Optimize a Byte-Level Representation for Automatic Speech Recognition ASR and Compare it with UTF-8 Representation: The blog discusses a new method for enhancing multilingual automatic speech recognition (ASR) using vector quantized auto-encoders. This approach improves byte-level representation accuracy, optimizes resource usage, and reduces error rates, outperforming UTF-8 and character-based methods in multilingual settings.➽ PowerLM-3B and PowerMoE-3B Released by IBM: Revolutionizing Language Models with 3 Billion Parameters and Advanced Power Scheduler for Efficient Large-Scale AI Training. IBM's PowerLM-3B and PowerMoE-3B models showcase advancements in large-scale language model training. Utilizing IBM’s Power scheduler, these models achieve high efficiency and scalability, optimizing learning rates and computational costs for improved performance in NLP tasks.➽ AtScale Open-Sourced Semantic Modeling Language (SML): Transforming Analytics with Industry-Standard Framework for Interoperability, Reusability, and Multidimensional Data Modeling Across Platforms: AtScale has open-sourced its Semantic Modeling Language (SML) to create a standardized, interoperable language for semantic modeling across platforms. Built on YAML, SML supports complex data structures, promotes reusability, and integrates with modern development practices, aiming to enhance collaboration and efficiency in analytics.➽ LG AI Research Open-Sources EXAONEPath: Transforming Histopathology Image Analysis with a 285M Patch-level Pre-Trained Model for Variety of Medical Prediction, Reducing Genetic Testing Time and Costs: LG AI Research's EXAONEPath enhances digital histopathology by addressing Whole Slide Image (WSI) challenges with advanced self-supervised learning and stain normalization. This open-source model improves diagnostic accuracy, reduces genetic testing time, and supports various medical tasks.🚀 Trendspotting: What's Next in Tech Trends➽ How to Trace Memory Allocation in Python? This tutorial demonstrates how to use Python's `tracemalloc` module for tracing memory allocation in memory-intensive operations. It covers setting up a sample dataset, tracking memory usage before and after processing, and comparing snapshots to debug memory issues.➽ Anomaly detection in streaming time series data with online learning using Amazon Managed Service for Apache Flink: This post describes building a real-time anomaly detection system for time series data using AWS services. It outlines how to deploy an end-to-end solution with Amazon Managed Service for Apache Flink, Kafka, and SageMaker, focusing on detecting unusual patterns in streaming data.🛠️ Platform Showdown: Comparing ML Tools & Services➽ 7 Free Cloud IDE for Data Science That You Are Missing Out: To start data science projects quickly, explore these 7 Cloud IDEs: Kaggle Notebooks, Deepnote, Lightning.ai, Datalab by DataCamp, Google Colab, Amazon SageMaker Studio Lab, and DataLore. Each provides pre-built environments and free access to GPUs.➽ Developing End-to-End Data Science Pipelines with Data Ingestion, Processing, and Visualization: The article discusses the iterative nature of data science projects, emphasizing the importance of data ingestion, processing, and visualization. It outlines an end-to-end process involving business understanding, data preparation, model building, and monitoring.➽ Optimizing MLOps for Sustainability: The post outlines optimizing MLOps for sustainability using AWS by improving data preparation, model training, and deployment. Key practices include selecting low-carbon impact regions, using efficient storage, leveraging SageMaker’s tools, and monitoring with AWS services to minimize resource use and emissions.📊 Success Stories: Real-World ML Case Studies➽ GraphRAG auto-tuning provides rapid adaptation to new domains: Microsoft Research's GraphRAG uses large language models to build domain-specific knowledge graphs from text, enabling complex query responses. The tool automates the creation of domain-specific prompts to enhance graph accuracy and streamline knowledge extraction.➽ The “Who Does What” Guide to Enterprise Data Quality: This analysis explores enterprise data quality management, focusing on roles and processes in data detection, triage, resolution, and measurement. It highlights the importance of foundational versus derived data products, and strategies for improving data quality and efficiency.➽ Can AI Agents Do Your Day-to-Day Tasks on Apps? The blog introduces AppWorld, a new benchmarking framework for AI agents that interact with various apps to perform complex tasks. It features a simulated environment, a benchmark of intricate tasks, and a robust evaluation framework to test and improve AI agents’ performance.🌍 ML Newsflash: Latest Industry Buzz & Discoveries➽ Google’s AI detective: The Needle in a Haystack test and how Gemini 1.5 Pro solves it. The blog discusses Google's Gemini 1.5 Pro, an AI model excelling in the "Needle in a Haystack" test. It showcases the model's ability to retrieve specific information from vast datasets across text, video, and audio, outperforming GPT-4 in complex retrieval tasks.➽ Regnology Automates Ticket-to-Code with GenAI on Vertex AI: The blog discusses Regnology's solution to the "Ticket-to-Code Problem," where bug reports are transformed into actionable code. Their Ticket-to-Code Writer tool, enhanced by Google’s Vertex AI and Gemini 1.5 Pro, automates this process, boosting efficiency by 60% and improving accuracy.➽ MedFuzz: Exploring the robustness of LLMs on medical challenge problems. LLMs excel in medical benchmarks but often oversimplify complex real-world scenarios. MedFuzz, inspired by security red-teaming and fuzzing, introduces adversarial challenges to test LLMs against these simplifying assumptions. This approach assesses their true effectiveness in nuanced clinical settings.*{box-sizing:border-box}body{margin:0;padding:0}a[x-apple-data-detectors]{color:inherit!important;text-decoration:inherit!important}#MessageViewBody a{color:inherit;text-decoration:none}p{line-height:inherit}.desktop_hide,.desktop_hide table{mso-hide:all;display:none;max-height:0;overflow:hidden}.image_block img+div{display:none}sub,sup{line-height:0;font-size:75%} @media (max-width: 100%;display:block}.mobile_hide{min-height:0;max-height:0;max-width: 100%;overflow:hidden;font-size:0}.desktop_hide,.desktop_hide table{display:table!important;max-height:none!important}} @media only screen and (max-width: 100%;} #pad-desktop {display: none !important;} }

Tableau Cookbook for Experienced ProfessionalsMaster AI in just 3 hours & become irreplaceable in 2025 (for free)2025 is already 25% over, and you’re not even 10% closer to your goals.But here’s your moment to flip the script.It’s time to learn the most in-demand skill of 2025 - AI -and finally take control of your time, growth, and impact.Save your free spot here (only 100 free seats)Join this FREE 3-hour AI Training (worth $399) - designed to help you master 20+ powerful AI tools and prompting techniques that can save you up to 16 hours a week.🚀 But there’s a catch: Only the first 100 people get it free.This hands-on course will teach you how to:👉 Automate tasks and save hours – Streamline your workflow and focus on what truly matters.👉 Make smarter, faster decisions – Use AI-driven insights to power up your business or career.👉 Grow your personal brand with AI – Create high-impact LinkedIn content in minutes.👉 Write like a pro with ChatGPT – Emails, reports, presentations… done in a fraction of the time.⏳ Spots are limited.🎁 First 100 seats are absolutely FREE.Ready to level up?Grab your free spot now before it's gone.Save Your Free Seat NowSponsoredSubscribe | Submit a tip | Advertise with usWelcome to DataPro 133 – Top Tools/Datasets Driving New Research 🔧📊, your weekly download on the breakthroughs redefining what AI and data teams can do. From OpenThoughts2-1M and Llama-Nemotron to DeepSeek-V3 and Meta’s Maverick, these new releases aren’t just datasets, they’re accelerators for reasoning, coding, and multimodal exploration.⚡ Also making waves: a bold new upgrade for data pros who’ve hit the Tableau plateau. The Tableau Cookbook for Experienced Professionals is now available for pre-order offering performance tuning, enterprise-ready governance, and the interactive magic your dashboards deserve.Whether you're scaling models, building agents, or sharpening your BI stack, this edition is stacked with what's next. Let’s dive in.Cheers,Merlyn ShelleyGrowth Lead, PacktWhy This Advanced Tableau Cookbook Is the Upgrade You Didn’t Know You NeededTableau Cookbook for Experienced ProfessionalsNow available for pre-order | Shipping April 25, 2025The Tableau Plateau: Why So Many Get StuckAt first, Tableau feels like magic.Drag, drop, and suddenly, your data tells a story.But fast forward a year, and the sparkle starts to fade:- Dashboards are slow and clunky- Your filters conflict, your data models sprawl- Stakeholders ask for secure access, and you realize you’ve hit a wallIt’s not that Tableau can’t do it.It’s that you’ve outgrown the basics.🔧 What Got You Here Won’t Get You ThereThat’s where Tableau Cookbook for Experienced Professionals steps in. Written by two experts who have trained Fortune 500 teams, led global analytics initiatives, and built enterprise-scale BI systems, this book offers a real-world-tested path to next-level Tableau mastery.👤 Pablo Sáenz de Tejada – Snowflake, Salesforce, The Information Lab👤 Daria Kirilenko – DSCOVR Analytics, Stanford UniversityThey’ve seen it all - from confident dashboard dabblers to elite data professionals. And they know the steps it takes to bridge that gap.🚀 The Three Shifts Every Advanced User Must Make PerformanceGo beyond visual appeal - build dashboards that are lightning fast and designed for scale.Learn:- Data model optimization- Tableau Cloud’s Data Management features- Performance troubleshooting with built-in tools InteractivityStop creating dashboards that “look good.” Start building tools users love to explore.Learn:- Zone visibility and advanced UX workflows- LOD expressions and table calculations- Layered interactivity through dynamic filters and tooltips GovernanceMaster Tableau in the enterprise arena. Secure it. Scale it. Own it.Learn:- REST API and TabPy integrations- Enterprise security strategies- Tableau’s Content Migration Tool (2025.1 and beyond)🛠️ Real-World Impact in ActionA global retailer’s dashboards were bloated and untrustworthy. After applying this book’s spatial join techniques and content structuring strategies, they reduced load time by 50%, streamlined permissions, and uncovered regional gaps in real-time sales.This book isn’t about “more charts.”It’s about building tools that drive real business decisions.✅ What You’ll UnlockHands-on recipes (60+) from senior consultants- Frameworks for troubleshooting, performance, and secure deployment- Advanced topics like TabPy, APIs, and scalable data modeling- A PDF eBook with purchase for on-the-go access🔓 Ready to Break Through?📅 Release Date: April 25, 2025🎁 Bonus templates and code samples for early buyers💡 Free PDF eBook with Kindle or print purchasePre-order NowTop Tools Driving New Research 🔧📊⭕ deepseek-ai/DeepSeek-V3-0324: DeepSeek introduced V3-0324 with enhanced reasoning (MMLU-Pro +5.3, GPQA +9.3, AIME +19.8), better code execution, improved Chinese writing, refined translation, more accurate function calling, and detailed search analysis. New system prompt and optimized temperature mapping included.⭕ ByteDance/InfiniteYou: ByteDance introduced InfiniteYou (InfU), leveraging Diffusion Transformers (DiTs) like FLUX for high-fidelity, identity-preserved image generation. InfU improves identity similarity, text-image alignment, and aesthetics using InfuseNet and multi-stage training. Two model variants, aes_stage2 (better aesthetics) and sim_stage1 (higher ID similarity), enhance flexibility.⭕ manycore-research/SpatialLM-Llama-1B: SpatialLM introduced SpatialLM-Llama-1B, a 3D large language model that processes point cloud data to generate structured 3D scene understanding. It identifies architectural elements (walls, doors, windows) and object bounding boxes. It supports multimodal inputs, enhancing applications in robotics and navigation.⭕ canopylabs/orpheus-3b-0.1-ft: Canopy Labs introduced Orpheus 3B 0.1 FT, a Llama-based speech model fine-tuned for high-quality, empathetic text-to-speech generation. It offers human-like intonation, zero-shot voice cloning, guided emotions, and low-latency real-time streaming, making it ideal for natural speech synthesis applications.⭕19 Git Tips For Everyday Use: The post shares practical Git commands and techniques to improve workflow efficiency. It covers logging, file extraction, rebasing, managing branches, fixing commits, using aliases, and troubleshooting, offering valuable insights for intermediate Git users.⭕ AI Expert Roadmap: This post offers an interactive collection of roadmaps covering AI, data science, machine learning, deep learning, and big data engineering. It guides learners on essential concepts, tools, and techniques while encouraging ongoing exploration of evolving technologies and best practices.⭕ Cookiecutter Data Science: The Cookiecutter Data Science v2 introduces an improved, standardized project structure for data science workflows. It offers a command-line tool (ccds) that simplifies project setup and enforces best practices. With enhanced functionality and flexible directory organization, it ensures consistency and reproducibility across projects.Topics Catching Fire in Data Circles 🔥💬⭕ Google DeepMind Researchers Propose CaMeL: A Robust Defense that Creates a Protective System Layer around the LLM, Securing It even when Underlying Models may be Susceptible to Attacks. Google DeepMind introduces CaMeL, a security layer that protects LLMs from prompt injection attacks without modifying the underlying models. Using a dual-model architecture and metadata-based policies, CaMeL isolates untrusted data, ensuring safer decision-making and outperforming existing defenses in security and reliability.⭕ A Code Implementation for Advanced Human Pose Estimation Using MediaPipe, OpenCV and Matplotlib: This tutorial demonstrates advanced human pose estimation using MediaPipe, OpenCV, and Matplotlib. It guides developers through detecting, visualizing, and extracting keypoints from images, enabling applications in sports, healthcare, and interactive systems. The code efficiently processes and annotates pose landmarks with high accuracy.⭕ Sea AI Lab Researchers Introduce Dr. GRPO: A Bias-Free Reinforcement Learning Method that Enhances Math Reasoning Accuracy in Large Language Models Without Inflating Responses: Sea AI Lab introduces Dr. GRPO, a bias-free reinforcement learning method that improves LLMs’ math reasoning accuracy without inflating responses. It eliminates response-length biases, ensuring fair model updates. Dr. GRPO-trained models outperformed others on key benchmarks while maintaining efficiency and reducing unnecessary verbosity.New Case Studies from the Tech Titans 🚀💡⭕ Anyscale powers AI compute for any workload using Google Compute Engine: Anyscale, built on Google Compute Engine (GCE) and Kubernetes Engine (GKE), powers scalable AI workloads across diverse environments. By optimizing compute flexibility and performance, it enables efficient model training, inference, and deployment. Anyscale reduces costs, boosts GPU utilization, and ensures reliable AI scaling across industries.⭕ Formula E’s AI equation: A new Driver Agent for the next generation of racers. Formula E partners with Google Cloud to introduce the AI-powered Driver Agent, leveraging Vertex AI and Gemini to analyze multimodal racing data. This tool democratizes access to data-led coaching, helping aspiring drivers refine performance by comparing their laps with professional benchmarks.⭕ Nuro drives autonomous innovation with AlloyDB for PostgreSQL: Nuro enhances autonomous vehicle innovation by migrating to AlloyDB for PostgreSQL, enabling seamless data management, high query performance, and vector similarity searches. This transition reduces operational costs, accelerates AI model training, and ensures continuous improvement of autonomous driving systems across complex real-world scenarios.⭕ Enhance deployment guardrails with inference component rolling updates for Amazon SageMaker AI inference: Amazon SageMaker AI introduces rolling updates for inference components, enhancing model deployment by reducing resource overhead, preventing downtime, and enabling batch-based updates with automatic rollback safeguards. This feature optimizes resource use and ensures reliable, cost-effective updates for GPU-heavy workloads, maintaining high availability in production environments.⭕ Integrate natural language processing and generative AI with relational databases: Amazon introduces a solution integrating natural language processing (NLP) and generative AI using Amazon Bedrock and Aurora PostgreSQL. It enables users to query relational databases using conversational language, reducing SQL complexity, democratizing data access, and easing the burden on developers through AI-driven SQL generation.Blog Pulse: What’s Moving Minds 🧠✨⭕ Automate Supply Chain Analytics Workflows with AI Agents usingn8n: n8n revolutionizes supply chain analytics by enabling AI-powered workflow automation without extensive coding. Using pre-built nodes, users can build AI agents to process emails, generate SQL queries, and update databases. This low-code platform empowers non-technical teams to maintain and enhance workflows efficiently.⭕ Uncertainty Quantification in Machine Learning with an Easy Python Interface: ML Uncertainty is a Python package that simplifies uncertainty quantification (UQ) for machine learning models, providing reliable prediction intervals with minimal code. Built on top of SciPy and scikit-learn, it enables users to estimate uncertainties efficiently, enhancing model interpretability and real-world decision-making.⭕ The Ultimate AI/ML Roadmap for Beginners: This post guides aspiring professionals through the essential steps to master AI and machine learning. Covering math fundamentals, Python, data structures, and algorithms, this roadmap equips learners to apply AI/ML in real-world scenarios without requiring a PhD.⭕ Attractors in Neural Network Circuits:Beauty and Chaos. This article explores how neural networks, when modeled as dynamical systems, evolve over time and converge to attractors, fixed points, limit cycles, or chaotic patterns. By adding feedback loops and nonlinear activations, even simple neural networks generate intricate behaviors, offering insights into memory formation, oscillating reactions, and chaotic processes.⭕ Least Squares: Where Convenience Meets Optimality. Least Squares is the cornerstone of regression models, primarily because of its simplicity, mathematical optimality, and deep connection with Maximum Likelihood Estimation (MLE). Beyond its computational ease, it minimizes Mean Squared Error (MSE) efficiently, derives the mean as a natural consequence of L2 minimization, and provides the Best Linear Unbiased Estimator (BLUE) when applied to Ordinary Least-Squares (OLS).*{box-sizing:border-box}body{margin:0;padding:0}a[x-apple-data-detectors]{color:inherit!important;text-decoration:inherit!important}#MessageViewBody a{color:inherit;text-decoration:none}p{line-height:inherit}.desktop_hide,.desktop_hide table{mso-hide:all;display:none;max-height:0;overflow:hidden}.image_block img+div{display:none}sub,sup{font-size:75%;line-height:0} @media (max-width: 100%;display:block}.mobile_hide{min-height:0;max-height:0;max-width: 100%;overflow:hidden;font-size:0}.desktop_hide,.desktop_hide table{display:table!important;max-height:none!important}}

Custom Tokenizer with Hugging Face Transformers, Multi-Agent Chat Application Using LangGraph @media only screen and (max-width: 100%;} #pad-desktop {display: none !important;} }Live Webinar: The Power of Data Storytelling in Driving Business Decisions (September 10, 2024 at 9 AM CST)Data doesn’t have to be overwhelming. Join our webinar to learn about Data Storytelling and turn complex information into actionable insights for faster decision-making.Click below to check the schedule in your time zone and secure your spot. Can't make it? Register to get the recording instead.REGISTER FOR FREESponsoredHappy Friday! 🌟Welcome to DataPro #110—Your Ultimate Data Science & ML Update! 🚀In the world of AI and ML, sharp reasoning is the key to smarter decisions and impactful leadership. Our latest insights and strategies will help you boost model accuracy, optimize performance, and cut costs with scalable solutions. Dive in for cutting-edge tips and real-world techniques to elevate your data game.📚 Book Haven: Top Reads & Author Insights◽"Data Science for Decision Makers": Elevate your leadership with data science and AI prowess by Jon Howells.◽"Data Science for IoT Engineers": Unlock data science techniques and ML applications for innovative IoT solutions by P. G. Madhavan.◽"Bash for Data Scientists": Master shell scripting for data science tasks with Oswald Campesato.◽"Angular and Machine Learning Pocket Primer": Get the essentials on integrating ML with Angular, also by Oswald Campesato.◽"AI, ML, and Deep Learning": Explore advanced AI techniques with Oswald Campesato’s practical guide.🔍 Model Breakdown: Algorithm of the Week◽Custom Tokenizers for Non-English Languages: Dive into Hugging Face Transformers for multilingual models.◽Concrete ML Privacy: Secure end-to-end privacy in model training and inference.◽Multilingual Multi-Agent Chat with LangGraph: Build diverse language chat applications.◽Approximating Stochastic Functions: Techniques for multivariate output functions.🪐Trendspotting: Hot Tech Trends◽Legal Reasoning Engines: How reasoning drives legal arguments.◽R Clinical Flowcharts with shinyCyJS: Use R for clinical flowcharting.◽Claude for Enterprise: Explore Anthropic's latest.◽IBM Quantum Update: Qiskit SDK v1.2 release news!🛠️ Platform Showdown: ML Tools & Services◽FastAPI for ML Web Apps: Build powerful web apps with FastAPI.◽DetoxBench: Benchmarking large language models for fraud and abuse detection.◽Llama-3.1-Storm-8B & CausalLM/miniG: New Hugging Face models.◽Build RAG Pipelines: Combine LlamaIndex with Amazon Bedrock for robust pipelines.📊 Success Stories: ML in Action◽Ecommerce Data Quality: Strategies for improving data quality.◽Essential Python Modules: Must-know Python modules for data engineers.◽Avoiding Data Science Mistakes: Tips to steer clear of common pitfalls.◽Thomson Reuters Labs: Accelerating AI/ML innovation with AWS MLOps.◽Galxe & AlloyDB: Cost-cutting success story.🌍 ML Newsflash: Industry Buzz & Discoveries◽GPT-4 for Customer Service: Redefining standards with GPT-4.◽HYGENE: A novel diffusion-based hypergraph generation method.◽Yi-Coder: Meet a compact yet powerful LLM for code.◽Guided Reasoning: New approaches to enhance multi-agent system intelligence.Enjoy the newsletter and have a fantastic weekend! ✨DataPro Newsletter is not just a publication; it’s a complete toolkit for anyone serious about mastering the ever-changing landscape of data and AI. Grab your copyand start transforming your data expertise today!Calling Data & ML Enthusiasts!Want to share your insights and build your online reputation? Contribute to our new Packt DataPro column! Discuss tools, share experiences, or ask questions. Gain recognition among 128,000+ data professionals and boost your CV. Simply reply with your Google Docs link or use our feedback form. Whether you’re looking for visibility or a discreet approach, we’re here to support you.Share your content today and engage with our vibrant community! We’re excited to hear from you!Take our weekly survey and get a free PDF copy of our best-selling book,"Interactive Data Visualization with Python - Second Edition."We appreciate your input and hope you enjoy the book!Share Your Insights and Shine! 🌟💬200+ hours of research on AI-led career growth strategies & hacks packed in 3 hoursThe only AI Crash Course you need to master 20+ AI tools, multiple hacks & prompting techniques in just 3 hoursYou’ll save 16 hours every week & find remote jobs using AI that will pay you upto $10,000/moRegister & save your seat now (100 free seats only)Sponsored📚 Book Haven: Must-Reads & Author InsightsDid you know? “Books are the quietest, most constant friends, holding the world’s treasured wisdom. They offer gentle guidance and timeless lessons, passing their rich inheritance from one generation to the next.”We’re thrilled to bring you this week’s must-have new releases, straight from the experts to your bookshelf! Whether you're eager to enhance your skills or explore new horizons, now is the perfect moment to add these invaluable resources to your collection.For a limited time,enjoy 30% off all eBooks at Packtpub.com. These books are thoughtfully crafted by industry insiders with hands-on experience, offering unique insights you won’t find anywhere else.Don’t let these Packt-exclusive deals slip away—seize the opportunity to learn from the best at an unbeatable price!Order Today at $24.99 $35.99Data Science for Decision Makers: Enhance your leadership skills with data science and AI expertiseBy Jon HowellsStruggling to bridge the gap between data science and business leadership? Our new book is here to help!What you’ll gain:✔️ Master statistics and ML to interpret models and drive decisions.✔️ Identify AI opportunities and oversee data projects from start to finish.✔️ Empower teams to tackle complex problems and build AI solutions.Elevate your leadership and make data work for you! 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This blog explains the importance of tokenization in NLP and provides a detailed guide on training a custom tokenizer for non-English languages using Hugging Face libraries, ensuring improved model performance for diverse datasets.➽ End-to-end privacy for model training and inference with Concrete ML: This blog explores how to achieve end-to-end privacy in collaborative machine learning using federated learning and fully homomorphic encryption (FHE). It details a demo with scikit-learn and Concrete ML for secure model training and inference.➽ Building a Multilingual Multi-Agent Chat Application Using LangGraph: This blog details the development of a multilingual chat application to bridge language barriers in workplaces. It covers building features using LangChain and LangGraph, including agent design, translation workflows, and deployment with FastAPI.➽ Approximating Stochastic Functions with Multivariate Outputs: The article describes an enhanced method for training generative machine learning models, named Pin Movement Training (PMT). It extends the original PMT, which approximated single-output stochastic functions, to handle multiple-output functions. The approach uses a neural network and a hypersphere-based Z-space to map and approximate multidimensional outputs, like autoencoders but with uniform sampling for better results.Developing for iOS? Setapp's 2024 report on the state of the iOS market in the EU is a must-seeHow do users in the EU find apps? What's the main source of information about new apps? Would users install your app from a third-party app marketplace?Set yourself up for success with these and more valuable marketing insights in Setapp Mobile's report iOS Market Insights for EU.Get Insights freeSponsored🚀 Trendspotting: What's Next in Tech Trends➽ Reasoning as the Engine Driving Legal Arguments: The article explores how tribunals assess evidence in legal cases, focusing on three key stages: determining evidence relevance, evaluating trustworthiness, and weighing competing evidence. It highlights the role of "reasoning sentences" in explaining decision-making and discusses machine learning techniques for identifying these sentences in legal documents.➽ Use R to build Clinical Flowchart with shinyCyJS: The blog discusses creating Clinical Flowcharts for visualizing clinical trials, focusing on various methods, particularly using R. It details challenges and solutions in drawing flowcharts, including software limitations and customizations with shinyCyJS for precise visual representation.➽ Claude for Enterprise \ Anthropic: The Claude Enterprise plan now offers enhanced features for secure collaboration, including a 500K context window, GitHub integration, and advanced security measures. This allows teams to leverage internal knowledge while safeguarding data.➽ IBM Quantum Computing - Release news: Qiskit SDK v1.2 is here! Qiskit SDK v1.2 introduces major updates, including Rust-based circuit infrastructure for faster performance, improved synthesis and transpilation, and new features. It also ends support for Python 3.8, requiring Python 3.9 or later. 🛠️ Platform Showdown: Comparing ML Tools & Services➽ Using FastAPI for Building ML-Powered Web Apps: This tutorial demonstrates building a machine learning web app using FastAPI and Jinja2 templates. It covers creating a prediction API for a Random Forest model and integrating it with a web interface for user interaction.➽ DetoxBench: Benchmarking large language models for multitask fraud & abuse detection. This paper introduces a benchmark suite to evaluate large language models (LLMs) for detecting and mitigating fraud and abuse in various real-world scenarios, highlighting performance gaps and offering a tool for improving LLMs in high-stakes applications.➽ Llama-3.1-Storm-8B · Hugging Face: The Llama-3.1-Storm-8B model outperforms Meta’s Llama-3.1-8B-Instruct and Hermes-3 across multiple benchmarks. It improves instruction-following, QA, reasoning, and function-calling via self-curation, fine-tuning, and model merging techniques.➽ CausalLM/miniG · Hugging Face: The miniG model has two versions: standard and "alt," the latter trained with masked context to improve stability. Trained on a large dataset with text and image support, it performs best with Hugging Face Transformers for minimal performance degradation.➽ Build powerful RAG pipelines with LlamaIndex and Amazon Bedrock: This blog explores using Retrieval Augmented Generation (RAG) techniques to enhance large language models (LLMs) by integrating external knowledge sources. It discusses building advanced RAG pipelines with LlamaIndex and Amazon Bedrock, covering topics like query routing, sub-question handling, and stateful agents.📊 Success Stories: Real-World ML Case Studies➽ Improving ecommerce data quality: This blog details how Lowe’s enhanced its website search accuracy by fine-tuning OpenAI’s GPT-3.5 model. By applying advanced prompt engineering, Lowe’s improved product data quality, reduced associate workload, and achieved a 20% accuracy boost in product tagging.➽ 10 Built-In Python Modules Every Data Engineer Should Know: This article highlights essential Python modules for data engineering, including tools for file management, data serialization, database interaction, and text processing. It covers how modules like `os`, `pathlib`, `shutil`, and `csv` can enhance data engineering tasks.➽ 5 Common Data Science Mistakes and How to Avoid Them: This blog outlines five common mistakes in data science projects, such as unclear objectives, neglecting basics, poor visualizations, lack of feature engineering, and overemphasizing accuracy. It offers practical solutions to avoid these pitfalls and improve project outcomes.➽ How Thomson Reuters Labs achieved AI/ML innovation at pace with AWS MLOps services? This post details how Thomson Reuters Labs developed a standardized MLOps framework using AWS SageMaker to streamline ML processes. It highlights the creation of TR MLTools and MLTools CLI to enhance efficiency, standardize practices, and accelerate AI/ML innovation.➽ Galxe migrates to AlloyDB for PostgreSQL, cutting costs by 40%: This blog explains how Galxe is addressing Web3 challenges by using AlloyDB for PostgreSQL and Google Cloud services. It highlights Galxe's innovations in decentralized identity, gamified user experiences, and scalable infrastructure to enhance Web3 adoption and performance.🌍 ML Newsflash: Latest Industry Buzz & Discoveries➽ Using GPT-4 to deliver a new customer service standard: Ada, valued at $1.2B with $200M in funding, is leading a $100B shift in customer service with its AI-native automation platform. Since its 2016 inception, Ada has doubled resolution rates using OpenAI’s GPT-4, achieving up to 80% resolution and setting new industry standards for effectiveness.➽ HYGENE: A Diffusion-based Hypergraph Generation Method. The paper introduces HYGENE, a diffusion-based method for generating realistic hypergraphs. Using a bipartite representation, it iteratively expands nodes and hyperedges through a denoising process, effectively modeling complex hypergraph structures. This is the first deep learning approach for hypergraph generation.➽ Meet Yi-Coder: A Small but Mighty LLM for Code. Yi-Coder is an open-source series of coding-focused LLMs, available in 1.5B and 9B parameter sizes. It offers advanced coding performance with up to 128K token context modeling, surpassing models like CodeQwen1.5 and DeepSeek-Coder, and excels in benchmarks such as LiveCodeBench and HumanEval.➽ Guided Reasoning: A New Approach to Improving Multi-Agent System Intelligence. Gregor Betz from Logikon AI introduces Guided Reasoning, a multi-agent system where a guide agent helps client agents improve their reasoning through structured methods. This approach, using argument maps and pros/cons evaluations, aims to enhance clarity and accuracy in AI decision-making and explanations.See you next time! *{box-sizing:border-box}body{margin:0;padding:0}a[x-apple-data-detectors]{color:inherit!important;text-decoration:inherit!important}#MessageViewBody a{color:inherit;text-decoration:none}p{line-height:inherit}.desktop_hide,.desktop_hide table{mso-hide:all;display:none;max-height:0;overflow:hidden}.image_block img+div{display:none}sub,sup{line-height:0;font-size:75%} @media (max-width: 100%;display:block}.mobile_hide{min-height:0;max-height:0;max-width: 100%;overflow:hidden;font-size:0}.desktop_hide,.desktop_hide table{display:table!important;max-height:none!important}} @media only screen and (max-width: 100%;} #pad-desktop {display: none !important;} }