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First Edition
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Language: English
Published by Apress, Incorporated, 2019
ISBN 10: 1484251067 ISBN 13: 9781484251065
Seller: Books Puddle, New York, NY, U.S.A.
Condition: New. 1st ed. edition NO-PA16APR2015-KAP.
Seller: preigu, Osnabrück, Germany
Taschenbuch. Condition: Neu. Agile Machine Learning | Effective Machine Learning Inspired by the Agile Manifesto | Eric Carter (u. a.) | Taschenbuch | xvii | Englisch | 2019 | Apress | EAN 9781484251065 | Verantwortliche Person für die EU: APress in Springer Science + Business Media, Heidelberger Platz 3, 14197 Berlin, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
Language: English
Published by Apress, Incorporated, 2019
ISBN 10: 1484251067 ISBN 13: 9781484251065
Seller: Majestic Books, Hounslow, United Kingdom
Condition: New. Print on Demand.
Language: English
Published by Apress, Incorporated, 2019
ISBN 10: 1484251067 ISBN 13: 9781484251065
Seller: Biblios, Frankfurt am main, HESSE, Germany
Condition: New. PRINT ON DEMAND.
Seller: AHA-BUCH GmbH, Einbeck, Germany
Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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.