Authors:
- 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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Table of contents (13 chapters)
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Front Matter
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Back Matter
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 engineeringteam 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.
Authors and Affiliations
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Kirkland, USA
Eric Carter
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Seattle, USA
Matthew Hurst
About the authors
Matthew Hurst is a Principal Engineering Manager and Applied Scientist currently working in the Machine Teaching group at Microsoft. He has worked in a number of teams in Microsoft including Bing Document Understanding, Local Search and in various innovation teams.
Bibliographic Information
Book Title: Agile Machine Learning
Book Subtitle: Effective Machine Learning Inspired by the Agile Manifesto
Authors: Eric Carter, Matthew Hurst
DOI: https://doi.org/10.1007/978-1-4842-5107-2
Publisher: Apress Berkeley, CA
eBook Packages: Professional and Applied Computing, Apress Access Books, Professional and Applied Computing (R0)
Copyright Information: Eric Carter, Matthew Hurst 2019
Softcover ISBN: 978-1-4842-5106-5Published: 22 August 2019
eBook ISBN: 978-1-4842-5107-2Published: 21 August 2019
Edition Number: 1
Number of Pages: XVII, 248
Number of Illustrations: 35 b/w illustrations
Topics: Microsoft and .NET, Software Engineering, Big Data