MLOps with Ray: Best Practices and Strategies for Adopting Machine Learning Operations (使用 Ray 的 MLOps：採用機器學習運營的最佳實踐與策略)

Luu, Hien, Pumperla, Max, Zhang, Zhe

MLOps with Ray: Best Practices and Strategies for Adopting Machine Learning Operations
  • 出版商: Apress
  • 出版日期: 2024-06-18
  • 售價: $2,190
  • 貴賓價: 9.5 折 $2,081
  • 語言: 英文
  • 頁數: 338
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 9798868803758
  • ISBN-13: 9798868803758
  • 相關分類: Machine Learning

    • 海外代購書籍(需單獨結帳)

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商品描述

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

商品描述(中文翻譯)

了解如何將 MLOps 作為一種工程學科，幫助快速且一致地將機器學習模型投入生產的挑戰。本書將幫助全球公司採用並將機器學習融入其流程和產品，以提升競爭力。

本書深入探討這一工程學科的各個方面和組成部分，並探索最佳實踐和案例研究。採用 MLOps 需要一個合理的策略，本書的早期章節將詳細介紹這一點。本書還討論了特徵工程、模型訓練、模型服務和機器學習可觀察性的基礎設施和最佳實踐。開源項目 Ray 提供了一個統一的框架和庫，以擴展機器學習工作負載和 Python 應用程序，並介紹了它如何融入 MLOps 技術棧。

本書旨在為機器學習從業者，如機器學習工程師和數據科學家，提供幫助，讓他們能夠通過採用、構建地圖和實踐 MLOps 來協助公司。

你將學到的內容：
- 瞭解 MLOps 學科
- 知曉 MLOps 技術棧及其組成部分
- 熟悉 MLOps 採用策略
- 理解特徵工程

本書適合對象：
專注於構建機器學習系統和基礎設施，以將 ML 模型投入生產的機器學習從業者、數據科學家和軟體工程師。

作者簡介

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.

作者簡介(中文翻譯)

Hien Luu 是一位充滿熱情的 AI/ML 工程領導者，自 2020 年以來一直在 DoorDash 領導機器學習平台。Hien 專注於為現實世界應用開發穩健且可擴展的 AI/ML 基礎設施。他是書籍《Beginning Apache Spark 3》的作者，並在 MLOps World、QCon（舊金山、紐約、倫敦）、GHC 2022、Data+AI Summit 等會議上擔任演講者。

Max Pumperla 是一位位於德國漢堡的數據科學教授和軟體工程師。他是一位活躍的開源貢獻者，維護多個 Python 套件，並且是機器學習書籍的作者。他目前在 Anyscale 擔任軟體工程師。作為 Pathmind Inc. 的產品研究負責人，他使用 Ray RLlib、Serve 和 Tune 開發工業應用的大規模強化學習解決方案。Max 曾是 Skymind 的 DL4J 核心開發者，並幫助擴展 Keras 生態系統。

Zhe Zhang 自 2020 年以來一直在 Anyscale 領導 Ray 工程團隊。在此之前，他在 LinkedIn 管理大數據/AI 計算團隊（提供 Hadoop/Spark/TensorFlow 作為服務）。Zhe 從事開源工作約十年。他是 Apache Hadoop 的提交者和 PMC 成員，也是 HDFS Erasure Coding 功能的主要作者，該功能是 Apache Hadoop 3.0 的關鍵部分。2020 年，Zhe 被選為 Apache Software Foundation 的成員。

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