Data science on the Google Cloud Platform : (Record no. 1479)
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000 -LEADER | |
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fixed length control field | 02799cam a2200469 i 4500 |
001 - CONTROL NUMBER | |
control field | 22938546 |
003 - CONTROL NUMBER IDENTIFIER | |
control field | OSt |
005 - DATE AND TIME OF LATEST TRANSACTION | |
control field | 20240224162538.0 |
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
fixed length control field | 230124s2022 mau 000 0 eng d |
010 ## - LIBRARY OF CONGRESS CONTROL NUMBER | |
LC control number | 2022439566 |
015 ## - NATIONAL BIBLIOGRAPHY NUMBER | |
National bibliography number | GBC244268 |
Source | bnb |
016 7# - NATIONAL BIBLIOGRAPHIC AGENCY CONTROL NUMBER | |
Record control number | 020518055 |
Source | Uk |
020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
International Standard Book Number | 9781098118952 |
Qualifying information | (pbk.) |
020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
International Standard Book Number | 1098118952 |
035 ## - SYSTEM CONTROL NUMBER | |
System control number | (OCoLC)on1308488176 |
040 ## - CATALOGING SOURCE | |
Original cataloging agency | UKMGB |
Language of cataloging | eng |
Transcribing agency | UKMGB |
Description conventions | rda |
Modifying agency | OCLCO |
-- | UKMGB |
-- | OCLCF |
-- | TOH |
-- | IVV |
-- | NVC |
-- | DLC |
042 ## - AUTHENTICATION CODE | |
Authentication code | lccopycat |
050 00 - LIBRARY OF CONGRESS CALL NUMBER | |
Classification number | QA76.585 |
Item number | .L35 2022 |
082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER | |
Classification number | 004.6782 LAK |
Edition number | 23 |
092 ## - LOCALLY ASSIGNED DEWEY CALL NUMBER (OCLC) | |
Edition number | 0 |
100 1# - MAIN ENTRY--PERSONAL NAME | |
Personal name | Lakshmanan, Valliappa, |
Relator term | author. |
245 10 - TITLE STATEMENT | |
Title | Data science on the Google Cloud Platform : |
Remainder of title | implementing end-to-end real-time data pipelines : from ingest to machine learning / |
Statement of responsibility, etc. | Valliappa Lakshmanan. |
250 ## - EDITION STATEMENT | |
Edition statement | Second edition. |
264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
Place of production, publication, distribution, manufacture | Cambridge : |
Name of producer, publisher, distributor, manufacturer | O'Reilly, |
Date of production, publication, distribution, manufacture, or copyright notice | 2022. |
300 ## - PHYSICAL DESCRIPTION | |
Extent | xvii, 440 pages : |
Other physical details | illustrations ; |
Dimensions | 24 cm. |
336 ## - CONTENT TYPE | |
Content type term | text |
Source | rdacontent |
337 ## - MEDIA TYPE | |
Media type term | unmediated |
Source | rdamedia |
338 ## - CARRIER TYPE | |
Carrier type term | volume |
Source | rdacarrier |
500 ## - GENERAL NOTE | |
General note | Previous edition: Sebastopol: O'Reilly, 2018. |
504 ## - BIBLIOGRAPHY, ETC. NOTE | |
Bibliography, etc. note | Includes bibliographical references and index. |
520 ## - SUMMARY, ETC. | |
Summary, etc. | Learn how easy it is to apply sophisticated statistical and machine learning methods to real-world problems when you build using Google Cloud Platform (GCP). This hands-on guide shows data engineers and data scientists how to implement an end-to-end data pipeline with cloud native tools on GCP. Throughout this updated second edition, you'll work through a sample business decision by employing a variety of data science approaches. Follow along by building a data pipeline in your own project on GCP, and discover how to solve data science problems in a transformative and more collaborative way. You'll learn how to: Employ best practices in building highly scalable data and ML pipelines on Google Cloud Automate and schedule data ingest using Cloud Run Create and populate a dashboard in Data Studio Build a real-time analytics pipeline using Pub/Sub, Dataflow, and BigQuery Conduct interactive data exploration with BigQuery Create a Bayesian model with Spark on Cloud Dataproc Forecast time series and do anomaly detection with BigQuery ML Aggregate within time windows with Dataflow Train explainable machine learning models with Vertex AI Operationalize ML with Vertex AI Pipelines. |
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
Topical term or geographic name entry element | Cloud computing. |
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
Topical term or geographic name entry element | Computing platforms. |
650 #6 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
Topical term or geographic name entry element | Infonuagique. |
650 #6 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
Topical term or geographic name entry element | Plateformes (Informatique) |
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
Topical term or geographic name entry element | Cloud computing. |
Source of heading or term | fast |
Authority record control number or standard number | (OCoLC)fst01745899 |
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
Topical term or geographic name entry element | Computing platforms. |
Source of heading or term | fast |
Authority record control number or standard number | (OCoLC)fst01893329 |
887 ## - NON-MARC INFORMATION FIELD | |
Source of data | CamTech Library |
906 ## - LOCAL DATA ELEMENT F, LDF (RLIN) | |
a | 7 |
b | cbc |
c | copycat |
d | 2 |
e | ncip |
f | 20 |
g | y-gencatlg |
942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
Source of classification or shelving scheme | Dewey Decimal Classification |
Koha item type | Books |
Suppress in OPAC | No |
No items available.