Agile Machine Learning
Language: English
Published by Apress, 2019
- Softcover
- New
Seller: moluna, Greven, Germanymoluna
AbeBooks seller since July 9, 2020
Condition: New
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Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Intermediate user level|Authors have proven real-world experience with numerous big data projects coordinated across distributed teams for multiple Microsoft marketsTeaches you how to manage projects involving machine learning more effectively in .
Seller Inventory # 297500179
- Title
- Agile Machine Learning
- Author
- Eric Carter|Matthew Hurst
- Publisher
- Apress
- Publication year
- 2019
- Condition
- New
- Binding
- Soft cover
- Language
- English
- ISBN 10
- 1484251067
- ISBN 13
- 9781484251065
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.
"Synopsis" may belong to another edition of this title.
About the Author
"About the title" may belong to another edition of this title.
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