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Agile Machine Learning

Effective Machine Learning Inspired by the Agile Manifesto

Authors: Carter, Eric, Hurst, Matthew

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  • Authors have proven real-world experience with numerous big data projects coordinated across distributed teams for multiple Microsoft markets
  • Teaches you how to manage projects involving machine learning more effectively in a production environment
  • Shows you, by example, how to deliver superior data products through agile processes and organize and manage a fast-paced team challenged with solving novel data problems at scale, in a production environment
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書籍の購入

イーブック ¥4,267
価格の適用国: Japan (日本円価格は個人のお客様のみ有効) (小計)
  • ISBN 978-1-4842-5107-2
  • ウォーターマーク付、 DRMフリー
  • ファイル形式: PDF, EPUB
  • どの電子書籍リーダーからでもすぐにお読みいただけます。
  • ご購入後、すぐにダウンロードしていただけます。
ソフトカバー ¥5,334
価格の適用国: Japan (日本円価格は個人のお客様のみ有効) (小計)
  • ISBN 978-1-4842-5106-5
  • 個人のお客様には、世界中どこでも配送料無料でお届けします。
  • Usually dispatched within 3 to 5 business days.
この書籍について

Build resilient applied machine learning teams that deliver better data products through adapting the guiding principles of the Agile Manifesto.

Bringing together talented people to create a great applied machine learning team is no small feat. With developers and data scientists both contributing expertise in their respective fields, communication alone can be a challenge. Agile Machine Learning teaches you how to deliver superior data products through agile processes and to learn, by example, how to organize and manage a fast-paced team challenged with solving novel data problems at scale, in a production environment.

The authors’ approach models the ground-breaking engineering principles described in the Agile Manifesto. The book provides further context, and contrasts the original principles with the requirements of systems that deliver a data product.


What You'll Learn

  • Effectively run a data engineering team that is metrics-focused, experiment-focused, and data-focused
  • Make sound implementation and model exploration decisions based on the data and the metrics
  • Know the importance of data wallowing: analyzing data in real time in a group setting
  • Recognize the value of always being able to measure your current state objectively
  • Understand data literacy, a key attribute of a reliable data engineer, from definitions to expectations


Who This Book Is For

Anyone who manages a machine learning team, or is responsible for creating production-ready inference components. Anyone responsible for data project workflow of sampling data; labeling, training, testing, improving, and maintaining models; and system and data metrics will also find this book useful. Readers should be familiar with software engineering and understand the basics of machine learning and working with data.

著者について

Eric Carter is Partner Group Engineering Manager on the Cortana Engineering team at Microsoft. In this role he works on search features around email and calendar, and implements new compliant versions of Cortana hosted in Azure and Microsoft's Office 365 substrate environments. He is also responsible for delivering frameworks, patterns, and practices for services development to other teams.

Matthew Hurst is Principal Architect on the Bing Local Search team at Microsoft. In this role he leads a team mining the web for knowledge about local businesses and attractions, serving local queries in multiple markets. He has both managed the team and acted as technical lead, and is involved in collaborating with multiple teams, including Microsoft Research.

Table of contents (13 chapters)

Table of contents (13 chapters)

書籍の購入

イーブック ¥4,267
価格の適用国: Japan (日本円価格は個人のお客様のみ有効) (小計)
  • ISBN 978-1-4842-5107-2
  • ウォーターマーク付、 DRMフリー
  • ファイル形式: PDF, EPUB
  • どの電子書籍リーダーからでもすぐにお読みいただけます。
  • ご購入後、すぐにダウンロードしていただけます。
ソフトカバー ¥5,334
価格の適用国: Japan (日本円価格は個人のお客様のみ有効) (小計)
  • ISBN 978-1-4842-5106-5
  • 個人のお客様には、世界中どこでも配送料無料でお届けします。
  • Usually dispatched within 3 to 5 business days.
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書誌情報

Bibliographic Information
Book Title
Agile Machine Learning
Book Subtitle
Effective Machine Learning Inspired by the Agile Manifesto
Authors
Copyright
2019
Publisher
Apress
Copyright Holder
Eric Carter, Matthew Hurst
イーブック ISBN
978-1-4842-5107-2
DOI
10.1007/978-1-4842-5107-2
ソフトカバー ISBN
978-1-4842-5106-5
Edition Number
1
Number of Pages
XVII, 248
Number of Illustrations
35 b/w illustrations
Topics