Machine Learning Engineering in Action

Wilson, Ben

  • 出版商: Manning
  • 出版日期: 2022-04-26
  • 定價: $2,100
  • 售價: 9.0$1,890
  • 語言: 英文
  • 頁數: 300
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 1617298719
  • ISBN-13: 9781617298714
  • 相關分類: Machine Learning
  • 相關翻譯: 機器學習項目交付實戰 (簡中版)
  • 立即出貨 (庫存 < 4)



Field-tested tips, tricks, and design patterns for building Machine Learning projects that are deployable, maintainable, and secure from concept to production.

Machine Learning Engineering in Action lays out an approach to building deployable, maintainable production machine learning systems. You'll adopt software development standards that deliver better code management, and make it easier to test, scale, and even reuse your machine learning code!

You'll learn how to plan and scope your project, manage cross-team logistics that avoid fatal communication failures, and design your code's architecture for improved resilience. You'll even discover when not to use machine learning--and the alternative approaches that might be cheaper and more effective. When you're done working through this toolbox guide, you'll be able to reliably deliver cost-effective solutions for organizations big and small alike.

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





購買印刷版書籍將包括一本免費的電子書(PDF、Kindle和ePub格式),由Manning Publications提供。


Ben Wilson has worked as a professional data scientist for more than ten years. He currently works as a resident solutions architect at Databricks, where he focuses on machine learning production architecture with companies ranging from 5-person startups to global Fortune 100. Ben is the creator and lead developer of the Databricks Labs AutoML project, a Scala-and Python-based toolkit that simplifies machine learning feature engineering, model tuning, and pipeline-enabled modeling.


Ben Wilson在過去十年中一直擔任專業的資料科學家。他目前在Databricks擔任住宅解決方案架構師,專注於與從5人初創公司到全球財富100強企業合作的機器學習生產架構。Ben是Databricks Labs AutoML項目的創建者和首席開發人員,該項目是一個基於Scala和Python的工具包,用於簡化機器學習特徵工程、模型調整和管道化建模。