Architecting Data and Machine Learning Platforms: Enable Analytics and AI-Driven Innovation in the Cloud (Record no. 98813)

MARC details
000 -LEADER
fixed length control field 02171 a2200181 4500
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 241125b |||||||| |||| 00| 0 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9789355428158
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 005.74015
Cutter TRA
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Tranquillin Marco
245 ## - TITLE STATEMENT
Title Architecting Data and Machine Learning Platforms: Enable Analytics and AI-Driven Innovation in the Cloud
250 ## - EDITION STATEMENT
Edition statement 1st Ed
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Name of publisher, distributor, etc Shroff Publishers & Distributors Pvt. Ltd.
Date of publication, distribution, etc 2023
Place of publication, distribution, etc Mumbai
300 ## - PHYSICAL DESCRIPTION
Extent 338
520 ## - Remark
Summary, etc All cloud architects need to know how to build data platforms that enable businesses to make data-driven decisions and deliver enterprise-wide intelligence in a fast and efficient way. This handbook shows you how to design, build, and modernize cloud native data and machine learning platforms using AWS, Azure, Google Cloud, and multicloud tools like Snowflake and Databricks.<br/><br/>Authors Marco Tranquillin, Valliappa Lakshmanan, and Firat Tekiner cover the entire data lifecycle from ingestion to activation in a cloud environment using real-world enterprise architectures. You'll learn how to transform, secure, and modernize familiar solutions like data warehouses and data lakes, and you'll be able to leverage recent AI/ML patterns to get accurate and quicker insights to drive competitive advantage.<br/><br/>You'll learn how to:<br/><br/>Design a modern and secure cloud native or hybrid data analytics and machine learning platform<br/>Accelerate data-led innovation by consolidating enterprise data in a governed, scalable, and resilient data platform<br/>Democratize access to enterprise data and govern how business teams extract insights and build AI/ML capabilities<br/>Enable your business to make decisions in real time using streaming pipelines<br/>Build an MLOps platform to move to a predictive and prescriptive analytics approach.<br/><br/>Source: https://www.amazon.in/Architecting-Data-Machine-Learning-Platforms/dp/9355428154/ref=sr_1_1?crid=3RP4UMMUK9M8F&dib=eyJ2IjoiMSJ9.y6gn3ZA5px4w7fFk8eEQtg.Hgt-jcbX9lQFb1y4TmnoicOLVU-Bl6SWeKxdaqRvtvs&dib_tag=se&keywords=9789355428158&qid=1732546256&sprefix=9789385889592%2Caps%2C368&sr=8-1
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Machine Learning
906 ## - LOCAL DATA ELEMENT F, LDF (RLIN)
a Business Analytics
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Item type Book
Source of classification or shelving scheme Dewey Decimal Classification
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Collection code Home library Current library Shelving location Date acquired Source of acquisition Cost, normal purchase price Collection Type Full call number Barcode Date last seen Cost, replacement price Koha item type
    Dewey Decimal Classification     Reference book Main Library Main Library Analytics 28/10/2024 Shroff Publishers 1480.00 Foreign Book 005.74015 TRA 119581 28/10/2024 1850.00 Book

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