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The Machine Learning Solutions Architect Handbook - Second Edition

You're reading from  The Machine Learning Solutions Architect Handbook - Second Edition

Product type Book
Published in Apr 2024
Publisher Packt
ISBN-13 9781805122500
Pages 602 pages
Edition 2nd Edition
Languages
Concepts
Author (1):
David Ping David Ping
Profile icon David Ping
LinkedIn
David Ping is an accomplished author and industry expert with over 28 years of experience in the field of data science and technology. He currently serves as the leader of a team of highly skilled data scientists and AI/ML solutions architects at AWS. In this role, he assists organizations worldwide in designing and implementing impactful AI/ML solutions to drive business success. David's extensive expertise spans a range of technical domains, including data science, ML solution and platform design, data management, AI risk, and AI governance. Prior to joining AWS, David held positions in renowned organizations such as JPMorgan, Credit Suisse, and Intel Corporation, where he contributed to the advancements of science and technology through engineering and leadership roles. With his wealth of experience and diverse skill set, David brings a unique perspective and invaluable insights to the field of AI/ML.
See other products by David Ping

Table of Contents (19) Chapters

Preface Navigating the ML Lifecycle with ML Solutions Architecture Exploring ML Business Use Cases Exploring ML Algorithms Data Management for ML Exploring Open-Source ML Libraries Kubernetes Container Orchestration Infrastructure Management Open-Source ML Platforms Building a Data Science Environment Using AWS ML Services Designing an Enterprise ML Architecture with AWS ML Services Advanced ML Engineering Building ML Solutions with AWS AI Services AI Risk Management Bias, Explainability, Privacy, and Adversarial Attacks Charting the Course of Your ML Journey Navigating the Generative AI Project Lifecycle Designing Generative AI Platforms and Solutions Other Books You May Enjoy
Index

Data cataloging

Data catalog plays a crucial role in data governance and enables data analysts and scientists to discover and access data stored in a central data storage. It becomes particularly important during the data understanding and exploration phase of the ML life cycle when scientists need to search and comprehend available data for their ML projects. When evaluating a data catalog technology, consider the following key factors:

  • Metadata catalog: The technology should support a central data catalog for effective management of data lake metadata. This involves handling metadata such as database names, table schemas, and table tags. The Hive metastore catalog is a popular standard for managing metadata catalogs.
  • Automated data cataloging: The capability to automatically discover and catalog datasets, as well as infer data schemas from various data sources like Amazon S3, relational databases, NoSQL databases, and logs. Typically, this functionality is implemented through a crawler...
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Authors (1)

Profile icon David Ping
LinkedIn
David Ping is an accomplished author and industry expert with over 28 years of experience in the field of data science and technology. He currently serves as the leader of a team of highly skilled data scientists and AI/ML solutions architects at AWS. In this role, he assists organizations worldwide in designing and implementing impactful AI/ML solutions to drive business success. David's extensive expertise spans a range of technical domains, including data science, ML solution and platform design, data management, AI risk, and AI governance. Prior to joining AWS, David held positions in renowned organizations such as JPMorgan, Credit Suisse, and Intel Corporation, where he contributed to the advancements of science and technology through engineering and leadership roles. With his wealth of experience and diverse skill set, David brings a unique perspective and invaluable insights to the field of AI/ML.
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See other products by David Ping

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