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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
Author (1):
David Ping David Ping
Profile icon David Ping

Table of Contents (19) Chapters

Preface 1. Navigating the ML Lifecycle with ML Solutions Architecture 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

Summary

You now have a solid understanding of various concepts such as AI, ML, and the essential steps of the end-to-end ML life cycle. Additionally, you have gained insight into the core functions of ML solutions architecture and how it plays a crucial role in the success of an ML project. With your newfound knowledge, you can differentiate between different types of ML and identify their application in solving business problems. Moreover, you have learned that it is crucial to have a deep understanding of business and data to achieve success in an ML project, besides modeling and engineering. Lastly, you have gained an understanding of the significance of ML solutions architecture and how it fits into the ML life cycle.In the upcoming chapter, we will dive into various ML use cases across different industries, such as financial services and media and entertainment, to gain further insights into the practical applications of ML.

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