Agile Machine Learning

Agile Machine Learning Front Cover
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248 pages

Book 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 the ground-breaking engineering principles described in the Agile Manifesto. The book provides further context, and contrasts the original principles with the requirements of 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 , 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 workflow of sampling data; labeling, training, , improving, and maintaining models; and system and data metrics will also find this book useful. Readers should be familiar with and understand the basics of machine learning and working with data.

Book Details

  • Title: Agile Machine Learning
  • Author: ,
  • Length: 248 pages
  • Edition: 1st ed.
  • Language: English
  • Publisher:
  • Publication Date: 2019-11-03
  • ISBN-10: 1484251067
  • ISBN-13: 9781484251065
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