BixBench to Evaluate AI Agents on Real-World Bioinformatics Task❯❯❯❯ 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📢 Welcome to DataPro #129 ~ Your Weekly Dose of Data Science & ML Innovation!The world of AI is evolving at lightning speed, and we’re here to keep you ahead of the curve! This week’s edition is packed with cutting-edge AI model evaluations, innovative MLOps tools, and groundbreaking advancements in agentic AI and retrieval-augmented generation (RAG).𖣠What’s Inside?🔍 Model Analysis & AI Performance – Explore how Vertex AI, LLM Comparator, and BentoML streamline AI evaluation and deployment.🧠 Advanced Reasoning Models – Dive into DeepSeek-R1’s reinforcement learning breakthroughs and OpenAI’s o1 model’s test-time compute scaling.🧪️Practical AI Use Cases – Learn how Unico is revolutionizing IDTech with Spanner Vector Search and how Agentic Knowledge Distillation enhances RAG efficiency.🎲MLOps & Data Science Essentials – Discover Python one-liners for Scikit-Learn, Streamlit for real-time crypto analysis, and the Defog AI’s Introspect.🤖 AI Alignment & Ethics – Tackle the growing concerns of deep scheming in agentic AI and why Intrinsic AI Alignment (IAIA) is critical for the future of responsible AI.Stay informed, stay innovative, and let’s dive into the latestdata and AIbreakthroughs together! 🚀Cheers,Merlyn ShelleyGrowth Lead, Packt❯❯❯❯ 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🔍 Fresh Insights ⋆✴︎˚｡⋆𖤐 Evaluate AI models with Vertex AI & LLM Comparator: This blog explores how to evaluate generative AI models using Vertex AI evaluation service and LLM Comparator. It explains pairwise model evaluation, a method to compare two models directly for better decision-making. The Vertex AI evaluation service helps with model selection, optimization, fine-tuning, and benchmarking, while the LLM Comparator offers an intuitive, human-in-the-loop approach for side-by-side comparisons. The post highlights how to define custom metrics, leverage automated and manual assessments, and streamline workflows with integrated tracking. Plus, new users can access $300 in free credit to test Google Cloud AI/ML services.𖤐 Time series forecasting with LLM-based foundation models and scalable AIOps on AWS: This blog explores how Chronos, an LLM-based foundation model, enhances time series forecasting with Amazon SageMaker Pipelines. Traditional forecasting requires extensive tuning, but Chronos leverages LLM architectures to generalize across domains and perform zero-shot predictions. The post covers integrating Chronos into SageMaker, generating synthetic data, fine-tuning, and optimizing models with hyperparameter search. Key highlights include reduced processing time, automated workflows, and scalable AIOps on AWS for improved forecasting efficiency. Readers will gain hands-on knowledge to streamline model deployment and enhance forecasting capabilities.𖤐 Manhattan Associates Discovers the Power of Deeply Connected Data Pipelines: Manhattan Associates streamlined data pipeline automation using CData Sync, overcoming connectivity issues and unpredictable costs. Key benefits include instant replication of 200+ Jira fields, agility in SQL Server data movement, and 50% cost savings with fixed pricing. CData Sync’s deep API connections enable scalable, error-free data integration across cloud and on-premises environments, eliminating the need for intensive monitoring. With efficient, connected pipelines, Manhattan Associates improved productivity, ensuring accurate, timely data for supply chain operations.𖤐 BentoML: MLOps for Beginners. This blog introduces BentoML, a beginner-friendly MLOps framework that simplifies model deployment with minimal DevOps expertise. It covers building a Text-to-Speech app, creating Docker images, and deploying models to BentoCloud using simple CLI commands. Readers learn how BentoML automates infrastructure, integrates with transformers, and scales AI services efficiently. The guide includes a hands-on tutorial for setting up, deploying, and monitoring machine learning models with GPU support for optimized inference.𖤐 10 Python One-Liners for Scikit-learn. This blog highlights 10 essential Python one-liners for Scikit-Learn, streamlining machine learning workflows. It covers data preprocessing, model training, evaluation, and automation with concise, efficient code. Learn how to import modules, split datasets, standardize features, train SVM models, perform PCA, generate reports, and build pipelines, all in just one line each. Ideal for quick experiments, prototyping, and simplifying repetitive tasks, these snippets help you write cleaner, more efficient code while improving model performance and workflow clarity.𖤐 Using GPT-4.5 Without a $200 Subscription: This blog reveals how to access GPT-4.5 without a $200 subscription using the OpenAI API Playground for as little as $0.10–$0.30 per request. It guides users through creating an OpenAI account, adding credits, selecting GPT-4.5-preview, and integrating the API into applications. While cost-effective, it remains one of OpenAI’s most expensive models, so users should consider it for high-value tasks. The article highlights GPT-4.5’s accuracy, human-like responses, and seamless API integration, making advanced AI more affordable for developers and AI enthusiasts.❯❯❯❯ 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🚀 Trendspotting: What's Next in Tech Trends𖤐 Beyond Monte Carlo Tree Search: Unleashing Implicit Chess Strategies with Discrete Diffusion. This blog explores DIFFUSEARCH, a discrete diffusion-based framework that enhances long-term planning in large language models (LLMs) without costly search algorithms like MCTS. Unlike traditional methods prone to error propagation, DIFFUSEARCH iteratively refines future predictions using diffusion models, improving decision accuracy and efficiency. Evaluated on chess games, it outperformed state-action models by 653 Elo, achieving higher accuracy with fewer data. Beyond chess, this implicit search method offers potential applications in AI planning, structured writing, and next-token prediction, marking a step forward in long-term reasoning for LLMs.𖤐 Forrester TEI study on Spanner shows benefits and cost savings: This blog explores the economic impact of Google Cloud’s Spanner, based on a Forrester TEI study, showing a 132% ROI over three years. Organizations benefit from $7.74M in cost savings, including $3.8M from retiring legacy databases, $1.2M from eliminating downtime, and $1M from reduced overprovisioning. Spanner’s scalability, reliability (99.999% uptime), and automation enable faster onboarding, improved budget predictability, and enhanced innovation. Beyond cost savings, it streamlines operations, reduces engineering workload, and supports agile development, making it a powerful alternative to legacy database systems.𖤐 Advancing biomedical discovery: Overcoming data challenges in precision medicine. This blog explores a Microsoft Research study on biomedical data challenges, highlighting data procurement issues, computational hurdles, and collaboration bottlenecks in precision medicine. Key recommendations include standardizing workflows, improving secure data-sharing, and leveraging AI for automation. A unified biomedical data lifecycle can enhance interoperability, reproducibility, and research efficiency. The study emphasizes cloud-based infrastructures to democratize data access and accelerate scientific discovery. By breaking data silos, researchers can advance individualized therapeutics, paving the way for more robust biomedical research and clinical innovation.𖤐 Researchers from FutureHouse and ScienceMachine Introduce BixBench: A Benchmark Designed to Evaluate AI Agents on Real-World Bioinformatics Task. BixBench evaluates AI performance in bioinformatics through 53 real-world analytical tasks, emphasizing multi-step reasoning. AI models like GPT-4o achieved only 17% accuracy, revealing challenges in scientific data analysis. This benchmark guides AI advancements in bioinformatics research.𖤐 Defog AI Open Sources Introspect: MIT-Licensed Deep-Research for Your Internal Data. Defog AI’s Introspect is an open-source AI tool that unifies structured and unstructured data research across SQL, PDFs, and web search. Using a Sonnet agent with recursive tool calling, it automates deep research, improving efficiency and insight extraction. Supporting major databases like PostgreSQL, Snowflake, and BigQuery, Introspect simplifies internal data analysis, reducing silos and manual effort. With an MIT license and active community, it’s a powerful solution for enterprises and developers looking to enhance AI-driven research and decision-making.𖤐 Unico builds cutting-edge IDTech with Spanner Vector Search: Unico, a leading biometric verification company, uses Google Cloud Spanner to power vector search for facial authentication. Handling 1.2 billion authentications, Unico prevents $14 billion in fraud and processes 35 million new faces monthly. Spanner’s vector search, with low latency, high accuracy (96%), and scalability, enables real-time fraud detection and secure identity verification. With Google Cloud’s support, Unico aims for global expansion, advancing AI-driven identity solutions beyond Brazil.𖤐 A Step by Step Guide to Deploy Streamlit App Using Cloudflared, BeautifulSoup, Pandas, Plotly for Real-Time Cryptocurrency Web Scraping and Visualization. This tutorial guides you through building and deploying a real-time cryptocurrency dashboard using Streamlit, BeautifulSoup, Pandas, and Plotly. It scrapes live crypto prices from CoinMarketCap, visualizes them with interactive charts, and deploys via Cloudflared for seamless public access. With bar and pie charts for price and market cap analysis, the app updates dynamically. Using Google Colab and Cloudflared, this approach ensures easy, authentication-free deployment, making it ideal for beginners and developers looking to create and share interactive data-driven web apps effortlessly.❯❯❯❯ 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🛠️ Platform Showdown: Comparing ML Tools & Services𖤐 Mastering 1:1s as a Data Scientist: From Status Updates to Career Growth: This blog explores effective 1:1 meetings for data scientists and analysts, covering regular scheduling, structured agendas, and key discussion topics. It emphasizes tracking achievements, resolving blockers, career growth discussions, and feedback exchanges. A well-prepared 1:1 document enhances communication, accountability, and performance reviews. Managers should align priorities, offer guidance, and foster career development. By integrating project updates, feedback loops, and company goals, these meetings strengthen relationships, boost productivity, and support long-term career progression in data teams.𖤐 Magma: A foundation model for multimodal AI agents across digital and physical worlds. Magma is a multimodal AI foundation model that integrates visual perception, language comprehension, and action reasoning across digital and physical environments. Unlike traditional VLA models, Magma enables AI agents and robots to generalize tasks efficiently, from UI navigation to real-world interactions. It introduces Set-of-Mark (SoM) and Trace-of-Mark (ToM) for structured task understanding and outperforms state-of-the-art models in zero-shot and finetuning evaluations. Available on Azure AI Foundry Labs and Hugging Face, Magma represents a step toward advanced AI-driven automation and decision-making.𖤐 Meet AI Co-Scientist: A Multi-Agent System Powered by Gemini 2.0 for Accelerating Scientific Discovery. The AI co-scientist, developed by Google Cloud AI, DeepMind, and Stanford, is a multi-agent system designed to accelerate biomedical discovery. It employs a "generate, debate, and evolve" framework using test-time compute scaling for improved hypothesis generation in drug repurposing, target discovery, and bacterial evolution. With specialized agents for ranking, clustering, and refining hypotheses, it achieves 78.4% top-1 accuracy and outperforms baseline models in novelty and impact. This AI-driven approach bridges disciplines, transforming scientific research collaboration and discovery.𖤐 DeepSeek AI Releases Smallpond: A Lightweight Data Processing Framework Built on DuckDB and 3FS. Smallpond, developed by DeepSeek AI, extends DuckDB into a distributed data processing framework using 3FS. It enables high-performance SQL analytics across large datasets without complex infrastructure. Supporting Python 3.8–3.12, Smallpond integrates Ray for parallel processing, offering scalability and flexibility. Benchmarked at 3.66TiB/min, it efficiently processes terabyte-scale data. With a lightweight, modular design, Smallpond simplifies distributed workflows, reducing maintenance overhead while maintaining high-throughput performance. As an open-source project, it fosters collaboration and innovation for modern data engineering.𖤐 IBM AI Releases Granite 3.2 8B Instruct and Granite 3.2 2B Instruct Models: Offering Experimental Chain-of-Thought Reasoning Capabilities. IBM Research AI introduces Granite 3.2, a family of instruction-tuned LLMs optimized for enterprise applications. The Granite 3.2-2B model prioritizes low-latency inference, while the 8B model delivers higher accuracy in structured tasks. Leveraging self-distillation and custom instruction tuning, these models achieve 82.6% accuracy in domain-specific retrieval and 97% reliability in multi-turn conversations. The 2B variant reduces latency by 35%, making it ideal for fast-response AI solutions. Released under Apache 2.0, Granite 3.2 provides a scalable, efficient alternative for business-ready AI deployment.𖤐 HippoRAG 2: Advancing Long-Term Memory and Contextual Retrieval in Large Language Models. HippoRAG 2, developed by Ohio State University and UIUC, enhances retrieval-augmented generation (RAG) by integrating structured knowledge graphs for improved factual recall and multi-hop reasoning. Using Personalized PageRank (PPR) and recognition memory, it boosts retrieval accuracy by 7% over leading models. Evaluated against BM25, GraphRAG, and LightRAG, it excels in QA, associative memory, and discourse understanding. By linking contextual information, HippoRAG 2 advances LLM continual learning, offering a neurobiology-inspired long-term memory framework that refines AI sense-making and reasoning capabilities.❯❯❯❯ 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📊 Success Stories: Real-World ML Case Studies𖤐 LLM + RAG: Creating an AI-Powered File Reader Assistant. This blog explores Retrieval-Augmented Generation (RAG), a technique that enhances LLMs by integrating external knowledge bases for more accurate, domain-specific responses. Unlike retraining large models, RAG dynamically retrieves relevant data at inference, reducing hallucinations and improving contextual accuracy. The article details a Streamlit-based AI-powered PDF reader, leveraging LangChain, OpenAI’s GPT-4, and FAISS for efficient document retrieval and Q&A. By embedding and vectorizing text, RAG enables structured information retrieval, making AI smarter and more adaptable for enterprise applications.𖤐 One-Tailed Vs. Two-Tailed Tests: This blog explores the differences between one-tailed and two-tailed hypothesis tests in A/B testing, explaining their impact on sample size, statistical power, and result interpretation. A one-tailed test detects a specific direction of change, requiring a smaller sample size, while a two-tailed test accounts for both positive and negative effects, offering greater flexibility but requiring more data. The choice depends on business objectives, with one-tailed tests favoring metric improvements and two-tailed tests ensuring unbiased evaluation. Understanding these trade-offs helps optimize testing strategies and resource allocation in data-driven decision-making.𖤐 Generative AI Is Declarative: This article explores how generative AI operates in a declarative mode, focusing on what users want rather than how to achieve it. Like ordering a cheeseburger, interactions with LLMs involve iterative refinement, as missing details are inferred rather than explicitly requested. Declarative AI interaction simplifies user experience but requires clear prompting strategies and evaluation mechanisms to ensure quality responses. Understanding general vs. non-general information helps optimize AI applications, balancing fresh data retrieval, privacy concerns, and structured prompts for better human-AI collaboration in real-world tasks.𖤐 Overcome Failing Document Ingestion & RAG Strategies with Agentic Knowledge Distillation: This blog explores Agentic Knowledge Distillation + Pyramid Search, a novel approach to improving Retrieval-Augmented Generation (RAG). By distilling critical information at ingestion, this method enhances retrieval efficiency, response accuracy, and scalability for complex, multi-document research tasks. It outperforms traditional RAG by reducing cognitive load, preserving context, and optimizing token usage, making AI-driven analysis more reliable and insightful.𖤐 The Urgent Need for Intrinsic Alignment Technologies for Responsible Agentic AI: This blog examines the emerging risks of deep scheming in AI, where autonomous AI agents manipulate actions and communications to achieve goals. It introduces Intrinsic AI Alignment (IAIA), a novel approach ensuring AI’s internal reasoning aligns with ethical principles, beyond external guardrails.𖤐 How to Train LLMs to “Think” (o1 & DeepSeek-R1)? This blog explores how DeepSeek-R1 replicated OpenAI’s o1 model’s advanced reasoning, detailing the use of reinforcement learning (RL), thinking tokens, and test-time compute scaling to improve LLMs’ problem-solving and decision-making capabilities.❯❯❯❯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❯❯❯❯ 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 $24.99We’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}}
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