Agile Machine Learning

Effective Machine Learning Inspired by the Agile Manifesto

By: Eric Carter, Matthew Hurst

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Paperback | 22 August 2019

At a Glance

Paperback
268 Pages

Dimensions(cm)
24.77 x 17.78 x 1.91

Paperback

$109.00

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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.

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