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Agile Machine Learning: Effective Machine Learning Inspired by the Agile Manifesto
Contributor(s): Carter, Eric (Author), Hurst, Matthew (Author)
ISBN: 1484251067     ISBN-13: 9781484251065
Publisher: Apress
OUR PRICE:   $71.99  
Product Type: Paperback - Other Formats
Published: August 2019
Qty:
Additional Information
BISAC Categories:
- Computers | Programming - Microsoft
- Computers | Software Development & Engineering - General
- Computers | Databases - General
Dewey: 004.165
Physical Information: 0.56" H x 7" W x 10" (1.04 lbs) 248 pages
 
Descriptions, Reviews, Etc.
Publisher Description:

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.