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Learn to build robust ML pipeline solutions for databricks transition

Master commonly available features like AutoML and MLflow

Leverage data governance and model deployment using MLflow model registry

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Description

Unleash the potential of databricks for end-to-end machine learning with this comprehensive guide, tailored for experienced data scientists and developers transitioning from DIY or other cloud platforms. Building on a strong foundation in Python, Practical Machine Learning on Databricks serves as your roadmap from development to production, covering all intermediary steps using the databricks platform. You’ll start with an overview of machine learning applications, databricks platform features, and MLflow. Next, you’ll dive into data preparation, model selection, and training essentials and discover the power of databricks feature store for precomputing feature tables. You’ll also learn to kickstart your projects using databricks AutoML and automate retraining and deployment through databricks workflows. By the end of this book, you’ll have mastered MLflow for experiment tracking, collaboration, and advanced use cases like model interpretability and governance. The book is enriched with hands-on example code at every step. While primarily focused on generally available features, the book equips you to easily adapt to future innovations in machine learning, databricks, and MLflow.

Who is this book for?

This book is for experienced data scientists, engineers, and developers proficient in Python, statistics, and ML lifecycle looking to transition to databricks from DIY clouds. Introductory Spark knowledge is a must to make the most out of this book, however, end-to-end ML workflows will be covered. If you aim to accelerate your machine learning workflows and deploy scalable, robust solutions, this book is an indispensable resource.

What you will learn

Transition smoothly from DIY setups to databricks

Master AutoML for quick ML experiment setup

Automate model retraining and deployment

Leverage databricks feature store for data prep

Use MLflow for effective experiment tracking

Gain practical insights for scalable ML solutions

Find out how to handle model drifts in production environments

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Instant access to your digital copy whilst your Print order is Shipped

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Redeem a companion digital copy on all Print orders

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Practical Machine Learning on Databricks

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Machine Learning Engineering with Python

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Amazon verified reviews

H2N Dec 14, 2023

Practical Machine Learning on Databricks is a great resource for data scientists and developers who want to use Databricks in machine learning in Python language . It guides readers with MLflow from data preparation, model selection, training to model employment with different hands on examples. The author gives helpful instruction with Databricks AutoML for efficient project management and collaboration techniques. An nice tool in Databricks-driven machine learning projects.

Amazon Verified review

S.Kundu Apr 24, 2024

Machine learning on Databricks accelerates insights with scalable processing. Its unified analytics platform seamlessly integrates data science workflows, leveraging distributed computing. With collaborative features, teams streamline model development and deployment. Databricks empowers experimentation with robust libraries and MLflow integration for efficient model tracking. From data ingestion to model deployment, Databricks simplifies the end-to-end machine learning lifecycle.Practical Machine Learning on Databricks is designed for developers and data scientists who want to leverage Databricks to work on machine learning projects. I have been looking for a book which can help me in implementing end-to-end machine learning projects in Databricks and this book definitely ended my search. This book assumes that you have knowledge in Python, statistics, machine learning cycles along with understanding of Spark. Although it is only around 200 pages book but it explains each and every concept practically starting from providing the overview till the deployment as a CICD pipeline.A few important topics of the book that I want to highlight are as below:The First part of the book starts with ML Process and its Challenges along with exploring Databricks and the Lakehouse architectureThe book will explain what is a Feature store and will help you in registering your feature table in Databricks Feature StoreThen it will help you to understand MLFlow Components on Databricks such as MLFlow Tracking, MLFlow Models and MLFlow Model Registry along with creation of Baseline Model using Databricks AutoMLThe book will cover the process of registering your model to Model Registry and deep dive into webhooks support in the Model Registry. It will also explain ML deployments and paradigms along with how you can deploy ML models for batch, streaming and real-time inferenceIt will then cover topics of how you can automate ML Workflows using Databricks Jobs to automate model training and testing.The final part covers concept of Model Drift and different techniques for drift detection along with implementation of drift detection on Databricks. It will also introduce you to MLOps and how you can deploy using different deployment patterns such as deploy models approach and deploy code approach

Dev Jan 24, 2024

This book gives a great overview of ML features on Databricks and helped me understand the parallels between DIY MLOps and how my model pipelines will look in Production. The code examples are also easy to understand. I recently cleared the Databricks ML professional certification. This book acted as a great guide! thanks

Arjun Feb 09, 2024

Excellent book for anyone interested in clearing the ML professional certification. Very well structured, great explanations followed up by clear examples!

tt0507 Jan 08, 2024

Practical Machine Learning on Databricks is a great resource for data scientists, developers, and ML engineers who want to use Databricks in their day-to-day workflow. The book focuses more on describing MLOps features on Databricks instead of model generation which may be great for ML engineers. The book provides extensive guides for ML features such as AutoML, Feature Store, and MLflow, which makes the book a perfect resource as these features are also supported by other MLOps services. Overall, I recommend this book due to its popularity of Databricks and extensive applications.

Practical Machine Learning on Databricks: Seamlessly transition ML models and MLOps on Databricks

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