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Agile Machine Learning

Effective Machine Learning Inspired by the Agile Manifesto

Authors: Carter, Eric, Hurst, Matthew

  • 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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Buy this book

eBook $34.99
price for USA (gross)
  • The eBook version of this title will be available soon
  • Due: October 2, 2019
  • ISBN 978-1-4842-5107-2
  • Digitally watermarked, DRM-free
  • Included format:
  • ebooks can be used on all reading devices
Softcover $44.99
price for USA
  • Customers within the U.S. and Canada please contact Customer Service at +1-800-777-4643, Latin America please contact us at +1-212-460-1500 (24 hours a day, 7 days a week).
  • Due: November 3, 2019
  • ISBN 978-1-4842-5106-5
  • Free shipping for individuals worldwide
About this book

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.

About the authors

Eric Carter has worked as Partner Group Engineering Manager on the Bing and Cortana teams at Microsoft. In these roles he worked on search features around products and reviews, business listings, email, and calendar. He currently works on the Microsoft Whiteboard product.

Matthew Hurst is Principal Engineering Manager and Applied Scientist currently working in the Machine Teaching group at Microsoft. He has worked on a number of teams in Microsoft, including Bing Document Understanding, Local Search, and on various innovation teams.


Buy this book

eBook $34.99
price for USA (gross)
  • The eBook version of this title will be available soon
  • Due: October 2, 2019
  • ISBN 978-1-4842-5107-2
  • Digitally watermarked, DRM-free
  • Included format:
  • ebooks can be used on all reading devices
Softcover $44.99
price for USA
  • Customers within the U.S. and Canada please contact Customer Service at +1-800-777-4643, Latin America please contact us at +1-212-460-1500 (24 hours a day, 7 days a week).
  • Due: November 3, 2019
  • ISBN 978-1-4842-5106-5
  • Free shipping for individuals worldwide

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Bibliographic Information

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
eBook ISBN
978-1-4842-5107-2
DOI
10.1007/978-1-4842-5107-2
Softcover ISBN
978-1-4842-5106-5
Edition Number
1
Number of Pages
XVII, 248
Number of Illustrations
35 b/w illustrations
Topics