MLOps with Ray: Best Practices and Strategies for Adopting Machine Learning Operations
Understand how to use MLOps as an engineering discipline to help with the challenges of bringing machine learning models to production quickly and consistently. This book will help companies worldwide to adopt and incorporate machine learning into their processes and products to improve their competitiveness.

The book delves into this engineering discipline's aspects and components and explores best practices and case studies. Adopting MLOps requires a sound strategy, which the book's early chapters cover in detail. The book also discusses the infrastructure and best practices of Feature Engineering, Model Training, Model Serving, and Machine Learning Observability. Ray, the open source project that provides a unified framework and libraries to scale machine learning workload and the Python application, is introduced, and you will see how it fits into the MLOps technical stack.

This book is intended for machine learning practitioners, such as machine learning engineers, and data scientists, who wish to help their company by adopting, building maps, and practicing MLOps.

What You'll Learn



• Gain an understanding of the MLOps discipline
• Know the MLOps technical stack and its components
• Get familiar with the MLOps adoption strategy
• Understand feature engineering

Who This Book Is For

Machine learning practitioners, data scientists, and software engineers who are focusing on building machine learning systems and infrastructure to bring ML models to production

1145345651
MLOps with Ray: Best Practices and Strategies for Adopting Machine Learning Operations
Understand how to use MLOps as an engineering discipline to help with the challenges of bringing machine learning models to production quickly and consistently. This book will help companies worldwide to adopt and incorporate machine learning into their processes and products to improve their competitiveness.

The book delves into this engineering discipline's aspects and components and explores best practices and case studies. Adopting MLOps requires a sound strategy, which the book's early chapters cover in detail. The book also discusses the infrastructure and best practices of Feature Engineering, Model Training, Model Serving, and Machine Learning Observability. Ray, the open source project that provides a unified framework and libraries to scale machine learning workload and the Python application, is introduced, and you will see how it fits into the MLOps technical stack.

This book is intended for machine learning practitioners, such as machine learning engineers, and data scientists, who wish to help their company by adopting, building maps, and practicing MLOps.

What You'll Learn



• Gain an understanding of the MLOps discipline
• Know the MLOps technical stack and its components
• Get familiar with the MLOps adoption strategy
• Understand feature engineering

Who This Book Is For

Machine learning practitioners, data scientists, and software engineers who are focusing on building machine learning systems and infrastructure to bring ML models to production

49.99 In Stock
MLOps with Ray: Best Practices and Strategies for Adopting Machine Learning Operations

MLOps with Ray: Best Practices and Strategies for Adopting Machine Learning Operations

MLOps with Ray: Best Practices and Strategies for Adopting Machine Learning Operations
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MLOps with Ray: Best Practices and Strategies for Adopting Machine Learning Operations

MLOps with Ray: Best Practices and Strategies for Adopting Machine Learning Operations

Overview

Understand how to use MLOps as an engineering discipline to help with the challenges of bringing machine learning models to production quickly and consistently. This book will help companies worldwide to adopt and incorporate machine learning into their processes and products to improve their competitiveness.

The book delves into this engineering discipline's aspects and components and explores best practices and case studies. Adopting MLOps requires a sound strategy, which the book's early chapters cover in detail. The book also discusses the infrastructure and best practices of Feature Engineering, Model Training, Model Serving, and Machine Learning Observability. Ray, the open source project that provides a unified framework and libraries to scale machine learning workload and the Python application, is introduced, and you will see how it fits into the MLOps technical stack.

This book is intended for machine learning practitioners, such as machine learning engineers, and data scientists, who wish to help their company by adopting, building maps, and practicing MLOps.

What You'll Learn



• Gain an understanding of the MLOps discipline
• Know the MLOps technical stack and its components
• Get familiar with the MLOps adoption strategy
• Understand feature engineering

Who This Book Is For

Machine learning practitioners, data scientists, and software engineers who are focusing on building machine learning systems and infrastructure to bring ML models to production

Product Details

ISBN-13: 9798868803758
Publisher: Apress
Publication date: 06/18/2024
Edition description: First Edition
Pages: 338
Product dimensions: 7.01(w) x 10.00(h) x (d)

About the Author

Hien Luu is a passionate AI/ML engineering leader who has been leading the Machine Learning platform at DoorDash since 2020. Hien focuses on developing robust and scalable AI/ML infrastructure for real-world applications. He is the author of the book Beginning Apache Spark 3 and a speaker at conferences such as MLOps World, QCon (SF, NY, London), GHC 2022, Data+AI Summit, and more.

Max Pumperla is a data science professor and software engineer located in Hamburg, Germany. He is an active open source contributor, maintainer of several Python packages, and author of machine learning books. He currently works as a software engineer at Anyscale. As head of product research at Pathmind Inc., he was developing reinforcement learning solutions for industrial applications at scale using Ray RLlib, Serve, and Tune. Max has been a core developer of DL4J at Skymind, and helped grow and extend the Keras ecosystem.

Zhe Zhang has been leading the Ray Engineering team at Anyscale since 2020. Before that, he was at LinkedIn, managing the Big Data/AI Compute team (providing Hadoop/Spark/TensorFlow as services). Zhe has been working on Open Source for about a decade. Zhe is a committer and PMC member of Apache Hadoop; and the lead author of the HDFS Erasure Coding feature, which is a critical part of Apache Hadoop 3.0. In 2020 Zhe was elected as a Member of the Apache Software Foundation.

Table of Contents

Chapter 1: Introduction to MLOps.- Chapter 2: MLOps Adoption Strategy and Case Studies.- Chapter 3: Feature Engineering Infrastructure.- Chapter 4: Model Training Infrastructure.- Chapter 5: Model Serving.- Chapter 6: Machine Learning Observability.- Chapter 7: Ray Core.- Chapter 8: Ray Air.- Chapter 9: The Future of MLOps.

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