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The Machine Learning Solutions Architect Handbook

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

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Product type Paperback
Published in Apr 2024
Publisher Packt
ISBN-13 9781805122500
Length 602 pages
Edition 2nd Edition
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Author (1):
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David Ping David Ping
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David Ping
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Table of Contents (19) Chapters Close

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

Who this book is for

This book is designed for two primary audiences: developers and cloud architects who are looking for guidance and hands-on learning materials to become ML solutions architects, and experienced ML architecture practitioners and data scientists who are looking to develop a broader understanding of industry ML use cases, enterprise data and ML architecture patterns, data management and ML tools, ML governance, and advanced ML engineering techniques. This book can also benefit data engineers and cloud system administrators looking to understand how data management and cloud system architecture fit into the overall ML platform architecture. Risk professionals, AI product managers, and technology decision makers will also benefit from topics on AI risk management, business AI use cases, and ML maturity journey and best practices.

This book assumes you have some Python programming knowledge and are familiar with AWS services. Some of the chapters are designed for ML beginners to learn the core ML fundamentals, and they might overlap with the knowledge already possessed by experienced ML practitioners.

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