Machine Learning Engineering with Python: Manage the production life cycle of machine learning models using MLOps with practical examples

McMahon, Andrew P.

Machine Learning Engineering with Python: Manage the production life cycle of machine learning models using MLOps with practical examples

相關主題

商品描述

Supercharge the value of your machine learning models by building scalable and robust solutions that can serve them in production environments

 

Key Features:

  • Explore hyperparameter optimization and model management tools
  • Learn object-oriented programming and functional programming in Python to build your own ML libraries and packages
  • Explore key ML engineering patterns like microservices and the Extract Transform Machine Learn (ETML) pattern with use cases

 

Book Description:

Machine learning engineering is a thriving discipline at the interface of software development and machine learning. This book will help developers working with machine learning and Python to put their knowledge to work and create high-quality machine learning products and services.

 

Machine Learning Engineering with Python takes a hands-on approach to help you get to grips with essential technical concepts, implementation patterns, and development methodologies to have you up and running in no time. You'll begin by understanding key steps of the machine learning development life cycle before moving on to practical illustrations and getting to grips with building and deploying robust machine learning solutions. As you advance, you'll explore how to create your own toolsets for training and deployment across all your projects in a consistent way. The book will also help you get hands-on with deployment architectures and discover methods for scaling up your solutions while building a solid understanding of how to use cloud-based tools effectively. Finally, you'll work through examples to help you solve typical business problems.

 

By the end of this book, you'll be able to build end-to-end machine learning services using a variety of techniques and design your own processes for consistently performant machine learning engineering.

 

What You Will Learn:

  • Find out what an effective ML engineering process looks like
  • Uncover options for automating training and deployment and learn how to use them
  • Discover how to build your own wrapper libraries for encapsulating your data science and machine learning logic and solutions
  • Understand what aspects of software engineering you can bring to machine learning
  • Gain insights into adapting software engineering for machine learning using appropriate cloud technologies
  • Perform hyperparameter tuning in a relatively automated way

 

Who this book is for:

This book is for machine learning engineers, data scientists, and software developers who want to build robust software solutions with machine learning components. If you're someone who manages or wants to understand the production life cycle of these systems, you'll find this book useful. Intermediate-level knowledge of Python is necessary.

商品描述(中文翻譯)

這本書的標題是《使用Python進行機器學習工程》。書中介紹了如何通過構建可擴展和穩健的解決方案，為機器學習模型增加價值，並在生產環境中使用它們。

書中的主要特點包括：
- 探索超參數優化和模型管理工具
- 學習使用Python進行面向對象編程和函數式編程，構建自己的機器學習庫和套件
- 探索微服務和ETML（Extract Transform Machine Learn）等關鍵機器學習工程模式，並提供使用案例

這本書將幫助從事機器學習和Python開發的開發人員將他們的知識應用到實際工作中，創建高質量的機器學習產品和服務。書中採用實踐方法，幫助讀者理解關鍵的機器學習開發生命周期步驟，並通過實際示例來建立和部署穩健的機器學習解決方案。

隨著進一步的學習，讀者將探索如何以一致的方式在所有項目中創建自己的訓練和部署工具集。本書還將幫助讀者深入了解部署架構，並發現如何有效使用基於雲的工具。最後，讀者將通過實例解決典型的業務問題。

通過閱讀本書，讀者將能夠使用各種技術構建端到端的機器學習服務，並設計自己的流程，實現一致的高性能機器學習工程。

本書的目標讀者包括機器學習工程師、數據科學家和軟件開發人員，他們希望使用機器學習組件構建穩健的軟件解決方案。如果你是管理這些系統的人，或者想了解它們的生產生命周期，這本書對你也很有用。需要具備中級水平的Python知識。

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