Agile Machine Learning: Effective Machine Learning Inspired by the Agile Manifesto
Language: English
Published by Apress, 2019
- Softcover
- New
Seller: Revaluation Books, Exeter, United KingdomRevaluation Books
AbeBooks seller since January 6, 2003
Condition: New
£ 65.63
Quantity: 2 available
Add to basketItem description from seller
248 pages. 9.75x7.00x0.75 inches. In Stock.
Seller Inventory # x-1484251067
- Title
- Agile Machine Learning: Effective Machine Learning Inspired by the Agile Manifesto
- Author
- Carter, Eric/ Hurst, Matthew
- Publisher
- Apress
- Publication year
- 2019
- Condition
- Brand New
- Binding
- Paperback
- Language
- English
- ISBN 10
- 1484251067
- ISBN 13
- 9781484251065
- Item weight
- 0.48 kilograms
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.
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About the Author
"About the title" may belong to another edition of this title.
Revaluation Books
Exeter, United Kingdom
AbeBooks seller since January 6, 2003
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