國立虎尾科技大學 |

Agile Machine Learning = Effective Machine Learning Inspired by the Agile Manifesto /

Record Type:	Language materials, printed : 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.
Description:	XVII, 248 p. 35 illus.online resource. :
Contained By:	Springer Nature eBook
Subject:	Microsoft software. -
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. - 1st ed. 2019. - XVII, 248 p. 35 illus.online resource.

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:

1253736
Microsoft software.

LC Class. No.: QA76.76.M52

Dewey Class. No.: 004.165

Agile Machine Learning = Effective Machine Learning Inspired by the Agile Manifesto /
LDR:03565nam a22003855i 4500 001 1013904
003 DE-He213
005 20200630072717.0
007 cr nn 008mamaa
008 210106s2019 xxu| s |||| 0|eng d
020 $a 9781484251072 $9 978-1-4842-5107-2
024 7 $a 10.1007/978-1-4842-5107-2 $2 doi
035 $a 978-1-4842-5107-2
050 4 $a QA76.76.M52
072 7 $a UMP $2 bicssc
072 7 $a COM051380 $2 bisacsh
072 7 $a UMP $2 thema
082 0 4 $a 004.165 $2 23
100 1 $a Carter, Eric. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 831271
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.
250 $a 1st ed. 2019.
264 1 $a Berkeley, CA : $b Apress : $b Imprint: Apress, $c 2019.
300 $a XVII, 248 p. 35 illus. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
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 Microsoft software. $3 1253736
650 0 $a Microsoft .NET Framework. $3 565417
650 0 $a Software engineering. $3 562952
650 0 $a Big data. $3 981821
650 1 4 $a Microsoft and .NET. $3 1114109
650 2 4 $a Software Engineering. $3 669632
650 2 4 $a Big Data. $3 1017136
700 1 $a Hurst, Matthew. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1308148
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9781484251065
776 0 8 $i Printed edition: $z 9781484251089
856 4 0 $u https://doi.org/10.1007/978-1-4842-5107-2
912 $a ZDB-2-CWD
912 $a ZDB-2-SXPC
950 $a Professional and Applied Computing (SpringerNature-12059)
950 $a Professional and Applied Computing (R0) (SpringerNature-43716)