ARCHBISHOP KAVUKATTU CENTRAL LIBRARY
ONLINE LIBRARY CATALOGUE (OPAC)

Introducing MLOps: (Record no. 44971)

MARC details
000 -LEADER
fixed length control field 02042nam a22002657a 4500
003 - CONTROL NUMBER IDENTIFIER
control field OSt
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20260817102840.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
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020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9789385889769
040 ## - CATALOGING SOURCE
Transcribing agency AKCL
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 006.31
Item number TRE-I
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Treveil, Mark
245 ## - TITLE STATEMENT
Title Introducing MLOps:
Remainder of title How to Scale Machine Learning in the Enterprise /
Statement of responsibility, etc. by Mark Treveil and the Dataiku Team
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Place of publication, distribution, etc. Mumbai:
Name of publisher, distributor, etc. Shroff Publishers and Distributors Pvt. Ltd.
Date of publication, distribution, etc. 2020
300 ## - PHYSICAL DESCRIPTION
Extent p.169
520 ## - SUMMARY, ETC.
Summary, etc. More than half of the analytics and machine learning (ML) models created by organizations today never make it into production. Some of the challenges and barriers to operationalization are technical, but others are organizational. Either way, the bottom line is that models not in production can't provide business impact.<br/><br/>This book introduces the key concepts of MLOps to help data scientists and application engineers not only operationalize ML models to drive real business change but also maintain and improve those models over time. Through lessons based on numerous MLOps applications around the world, nine experts in machine learning provide insights into the five steps of the model life cycle--Build, Preproduction, Deployment, Monitoring, and Governance--uncovering how robust MLOps processes can be infused throughout.<br/><br/>This book helps you:<br/><br/>Fulfill data science value by reducing friction throughout ML pipelines and workflows<br/>Refine ML models through retraining, periodic tuning, and complete remodeling to ensure long-term accuracy<br/>Design the MLOps life cycle to minimize organizational risks with models that are unbiased, fair, and explainable<br/>Operationalize ML models for pipeline deployment and for external business systems that are more complex and less standardized
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Computer Science
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Information
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Computing and Information
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Artificial Intelligence
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Machine Learning
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type Books
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme Dewey Decimal Classification
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme Dewey Decimal Classification
Suppress in OPAC No
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Home library Current library Date acquired Cost, normal purchase price Total Checkouts Full call number Barcode Date last seen Cost, replacement price Price effective from Koha item type
    Dewey Decimal Classification     ARCHBISHOP KAVUKATTU CENTRAL LIBRARY ARCHBISHOP KAVUKATTU CENTRAL LIBRARY 07/14/2026 750.00   006.31 TRE-I 69336 07/14/2026 750.00 07/14/2026 Books