Operationalizing Machine Learning Pipelines : (Record no. 11588)

MARC details
000 -LEADER
fixed length control field 01723nam a22002897i 4500
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20250819135114.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 250807s2020 ii a|||fr|||| 00| 0 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9789355510235
040 ## - CATALOGING SOURCE
Original cataloging agency EG-GaU‬‬
Transcribing agency EG-GaU‬‬
Modifying agency EG-GaU‬‬
Description conventions rda
082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER
Edition number 23
Classification number 005.4
Item number P.V.O
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Pandey, Vishwajyoti,
Relator term author.
9 (RLIN) 61820
245 10 - TITLE STATEMENT
Title Operationalizing Machine Learning Pipelines :
Remainder of title Building Reusable and Reproducible Machine Learning Pipelines Using MLOps /
Statement of responsibility, etc. Vishwajyoti Pandey & Shaleen Bengani.
250 ## - EDITION STATEMENT
Edition statement first edition.
264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE
Place of production, publication, distribution, manufacture New Delhi :
Name of producer, publisher, distributor, manufacturer BPB Publications,
Date of production, publication, distribution, manufacture, or copyright notice 2022.
300 ## - PHYSICAL DESCRIPTION
Extent 162 pages :
Other physical details illustrations ;
Dimensions 23 cm.
336 ## - CONTENT TYPE
Source rdacontent
Content type term text
Content type code txt
337 ## - MEDIA TYPE
Source rdamedia
Media type term unmediated
Media type code n
338 ## - CARRIER TYPE
Source rdacarrier
Carrier type term volume
Carrier type code nc
504 ## - BIBLIOGRAPHY, ETC. NOTE
Bibliography, etc. note Includes practical case studies, tool examples, and key feature highlights.<br/>
505 ## - FORMATTED CONTENTS NOTE
Formatted contents note DS/ML Projects – Initial Setup; ML Projects Lifecycle; ML Architecture – Framework & Components; Data Exploration & Problem Quantification; Training & Testing ML Models; Model Performance Measurement; CRUD with JS Frameworks; Feature Store; Building ML Pipeline.<br/>
520 ## - SUMMARY, ETC.
Summary, etc. A practitioner’s guide to implementing end-to-end MLOps workflows. Covers features such as GitOps automation, feature store creation, serverless pipelines, model serving with KFServing and Polyaxon, and model monitoring in production—ideal for ML engineers, data scientists, and DevOps professionals.turn0search3turn0search12<br/>
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Machine learning
General subdivision implementation.
9 (RLIN) 60427
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Data engineering
General subdivision pipelines.
9 (RLIN) 61821
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Bengani, Shaleen,
Relator term author.
9 (RLIN) 61822
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme Dewey Decimal Classification
Koha item type Books
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Home library Current library Shelving location Date acquired Total Checkouts Full call number Barcode Date last seen Copy number Price effective from Koha item type
    Dewey Decimal Classification     Media and mass communication Library Media and mass communication Library B2 08/19/2025   005.4 P.V.O MA0002223 08/19/2025 C.1 08/19/2025 Books