Machine Learning Systems: Designs that scale

Jeff Smith

  • 出版商: Manning
  • 出版日期: 2018-07-08
  • 售價: $1,575
  • 貴賓價: 9.5$1,496
  • 語言: 英文
  • 頁數: 224
  • 裝訂: Paperback
  • ISBN: 1617293334
  • ISBN-13: 9781617293337
  • 相關分類: Machine Learning
  • 相關翻譯: 機器學習系統 (簡中版)
  • 立即出貨 (庫存 < 4)

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商品描述

Summary

Machine Learning Systems: Designs that scale is an example-rich guide that teaches you how to implement reactive design solutions in your machine learning systems to make them as reliable as a well-built web app.

Foreword by Sean Owen, Director of Data Science, Cloudera

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

About the Technology

If you're building machine learning models to be used on a small scale, you don't need this book. But if you're a developer building a production-grade ML application that needs quick response times, reliability, and good user experience, this is the book for you. It collects principles and practices of machine learning systems that are dramatically easier to run and maintain, and that are reliably better for users.

About the Book

Machine Learning Systems: Designs that scale teaches you to design and implement production-ready ML systems. You'll learn the principles of reactive design as you build pipelines with Spark, create highly scalable services with Akka, and use powerful machine learning libraries like MLib on massive datasets. The examples use the Scala language, but the same ideas and tools work in Java, as well.

What's Inside

 

  • Working with Spark, MLlib, and Akka
  • Reactive design patterns
  • Monitoring and maintaining a large-scale system
  • Futures, actors, and supervision

About the Reader

Readers need intermediate skills in Java or Scala. No prior machine learning experience is assumed.

About the Author

Jeff Smith builds powerful machine learning systems. For the past decade, he has been working on building data science applications, teams, and companies as part of various teams in New York, San Francisco, and Hong Kong. He blogs (https://medium.com/@jeffksmithjr), tweets (@jeffksmithjr), and speaks (www.jeffsmith.tech/speaking) about various aspects of building real-world machine learning systems.

Table of Contents

 

PART 1 - FUNDAMENTALS OF REACTIVE MACHINE LEARNING

PART 2 - BUILDING A REACTIVE MACHINE LEARNING SYSTEM

PART 3 - OPERATING A MACHINE LEARNING SYSTEM

  1. Learning reactive machine learning
  2. Using reactive tools
  3. Collecting data
  4. Generating features
  5. Learning models
  6. Evaluating models
  7. Publishing models
  8. Responding
  9. Delivering
  10. Evolving intelligence

商品描述(中文翻譯)


摘要

機器學習系統:可擴展的設計是一本以豐富範例為基礎的指南,教導您如何在機器學習系統中實施反應式設計解決方案,使其像建立良好的網頁應用程式一樣可靠。

Sean Owen(Cloudera資料科學總監)撰寫的前言

購買印刷版書籍將包含Manning Publications提供的PDF、Kindle和ePub格式的免費電子書。

關於技術

如果您正在建立用於小規模使用的機器學習模型,則不需要閱讀本書。但如果您是一位開發人員,正在建立需要快速響應時間、可靠性和良好用戶體驗的生產級ML應用程式,那麼這本書就是為您而寫的。它匯集了機器學習系統的原則和實踐,這些系統運行和維護起來明顯更容易,並且對用戶更可靠。

關於本書

機器學習系統:可擴展的設計教導您如何設計和實施可投入生產的ML系統。您將學習反應式設計原則,並使用Spark建立流程,使用Akka創建高度可擴展的服務,並在大型數據集上使用強大的MLib機器學習庫。範例使用Scala語言,但相同的思想和工具也適用於Java。

內容簡介

 


  • 使用Spark、MLlib和Akka

  • 反應式設計模式

  • 監控和維護大型系統

  • Future、Actor和監督

讀者對象

讀者需要具備Java或Scala的中級技能,不需要先前的機器學習經驗。

作者簡介

Jeff Smith擅長建立強大的機器學習系統。在過去的十年中,他一直在紐約、舊金山和香港的不同團隊中建立數據科學應用、團隊和公司。他在博客(https://medium.com/@jeffksmithjr)、推特(@jeffksmithjr)和演講(www.jeffsmith.tech/speaking)上分享有關構建實際機器學習系統的各個方面的內容。

目錄

 

第1部分 - 反應式機器學習基礎

第2部分 - 構建反應式機器學習系統

第3部分 - 運營機器學習系統


  1. 學習反應式機器學習

  2. 使用反應式工具

  3. 收集數據

  4. 生成特徵

  5. 學習模型

  6. 評估模型

  7. 發布模型

  8. 響應

  9. 交付

  10. 演進智能