You're reading from The Machine Learning Solutions Architect Handbook Practical strategies and best practices on the ML lifecycle, system design, MLOps, and generative AI

Product type Paperback

Published in Apr 2024

Publisher Packt

ISBN-13 9781805122500

Length 602 pages

Edition 2nd Edition

Languages

Python

Tools

MLOps

Concepts

Machine Learning

Author (1):

David Ping

View More author details

Table of Contents (19) Chapters

Preface

1. Navigating the ML Lifecycle with ML Solutions Architecture FREE CHAPTER

2. Exploring ML Business Use Cases

3. Exploring ML Algorithms

4. Data Management for ML

5. Exploring Open-Source ML Libraries

6. Kubernetes Container Orchestration Infrastructure Management

7. Open-Source ML Platforms

8. Building a Data Science Environment Using AWS ML Services

9. Designing an Enterprise ML Architecture with AWS ML Services

10. Advanced ML Engineering

11. Building ML Solutions with AWS AI Services

12. AI Risk Management

13. Bias, Explainability, Privacy, and Adversarial Attacks

14. Charting the Course of Your ML Journey

15. Navigating the Generative AI Project Lifecycle

16. Designing Generative AI Platforms and Solutions

17. Other Books You May Enjoy

18. 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...