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Agile machine learning = effective m...

Carter, Eric.

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Agile machine learning = effective machine learning inspired by the agile manifesto /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Agile machine learning/ by Eric Carter, Matthew Hurst.
Reminder of title:	effective machine learning inspired by the agile manifesto /
Author:	Carter, Eric.
other author:	Hurst, Matthew.
Published:	Berkeley, CA :Apress : : 2019.,
Description:	xvii, 248 p. :ill., digital ;24 cm.
[NT 15003449]:	Chapter 1: Early Delivery -- Chapter 2: Changing Requirements -- Chapter 3: Continuous Delivery -- Chapter 4: Aligning with the Business -- Chapter 5: Motivated Individuals -- Chapter 6: Effective Communication -- Chapter 7: Monitoring -- Chapter 8: Sustainable Development -- Chapter 9: Technical Excellence -- Chapter 10 Simplicity -- Chapter 11: Self-organizing Teams -- Chapter 12: Tuning and Adjusting -- Chapter 13: Conclusion.
Contained By:	Springer Nature eBook
Subject:	Machine learning. -
Online resource:	https://doi.org/10.1007/978-1-4842-5107-2
ISBN:	9781484251072

Agile machine learning = effective machine learning inspired by the agile manifesto /
Carter, Eric.

Agile machine learningeffective machine learning inspired by the agile manifesto /[electronic resource] :by Eric Carter, Matthew Hurst. - Berkeley, CA :Apress :2019. - xvii, 248 p. :ill., digital ;24 cm.

Chapter 1: Early Delivery -- Chapter 2: Changing Requirements -- Chapter 3: Continuous Delivery -- Chapter 4: Aligning with the Business -- Chapter 5: Motivated Individuals -- Chapter 6: Effective Communication -- Chapter 7: Monitoring -- Chapter 8: Sustainable Development -- Chapter 9: Technical Excellence -- Chapter 10 Simplicity -- Chapter 11: Self-organizing Teams -- Chapter 12: Tuning and Adjusting -- Chapter 13: Conclusion.

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 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.

ISBN: 9781484251072

Standard No.: 10.1007/978-1-4842-5107-2doiSubjects--Topical Terms:

533906
Machine learning.

LC Class. No.: Q325.5 / .C37 2019

Dewey Class. No.: 006.31

Agile machine learning = effective machine learning inspired by the agile manifesto /
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245 1 0 $a Agile machine learning $h [electronic resource] : $b effective machine learning inspired by the agile manifesto / $c by Eric Carter, Matthew Hurst.
260 $a Berkeley, CA : $b Apress : $b Imprint: Apress, $c 2019.
300 $a xvii, 248 p. : $b ill., digital ; $c 24 cm.
505 0 $a Chapter 1: Early Delivery -- Chapter 2: Changing Requirements -- Chapter 3: Continuous Delivery -- Chapter 4: Aligning with the Business -- Chapter 5: Motivated Individuals -- Chapter 6: Effective Communication -- Chapter 7: Monitoring -- Chapter 8: Sustainable Development -- Chapter 9: Technical Excellence -- Chapter 10 Simplicity -- Chapter 11: Self-organizing Teams -- Chapter 12: Tuning and Adjusting -- Chapter 13: Conclusion.
520 $a 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 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.
650 0 $a Machine learning. $3 533906
650 1 4 $a Microsoft and .NET. $3 3134847
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