Agile Machine Learning: (Record no. 694601)

MARC details
000 -LEADER
fixed length control field 02375nam a2200181 4500
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 9781484276785
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 006.31
Item number CAR
084 ## - OTHER CLASSIFICATION NUMBER
Source of Number Colon Classification
100 ## - MAIN ENTRY--AUTHOR NAME
Personal name Carter, Eric
245 ## - TITLE STATEMENT
Title Agile Machine Learning:
Sub Title Effective Machine Learning Inspired by the Agile Manifesto/
Statement of responsibility, etc Eric Carter, Matthew Hurst
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Place of publication California:
Name of publisher Apress,
Year of publication 2019.
300 ## - PHYSICAL DESCRIPTION
Number of Pages xvii, 248 p.
520 ## - SUMMARY, ETC.
Summary, etc Build resilient applied machine learning teams that deliver better data products through adapting the guiding principles of the Agile Manifesto.<br/><br/>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.<br/><br/>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.<br/> What You'll Learn<br/><br/> Effectively run a data engineering team that is metrics-focused, experiment-focused, and data-focused<br/> Make sound implementation and model exploration decisions based on the data and the metrics<br/> Know the importance of data wallowing: analyzing data in real time in a group setting<br/> Recognize the value of always being able to measure your current state objectively<br/> Understand data literacy, a key attribute of a reliable data engineer, from definitions to expectations<br/><br/><br/>Who This Book Is For<br/><br/>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 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical Term Agile Machine Learning
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical Term Applied machine learning
700 ## - ADDED ENTRY--PERSONAL NAME
Personal name Hurst, Matthew
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type Book
Holdings
Withdrawn status Lost status Damaged status Not for loan Home Library Current Location Shelving location Date acquired Full call number Accession Number Price effective from Koha item type
        Dept. of Futures Studies Dept. of Futures Studies General Stacks 01/08/2023 006.31 CAR DFS4614 01/08/2023 Book