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Agile Machine Learning : Effective Machine Learning Inspired by the Agile Manifesto, Paperback / softback Book

Agile Machine Learning : Effective Machine Learning Inspired by the Agile Manifesto Paperback / softback

Paperback / softback

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 LearnEffectively run a data engineeringteam that is metrics-focused, experiment-focused, and data-focusedMake sound implementation and model exploration decisions based on the data and the metricsKnow the importance of data wallowing: analyzing data in real time in a group settingRecognize the value of always being able to measure your current state objectivelyUnderstand data literacy, a key attribute of a reliable data engineer, from definitions to expectationsWho This Book Is ForAnyone 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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