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Google Machine Learning and Generative AI for Solutions Architects

You're reading from   Google Machine Learning and Generative AI for Solutions Architects ​Build efficient and scalable AI/ML solutions on Google Cloud

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Product type Paperback
Published in Jun 2024
Publisher Packt
ISBN-13 9781803245270
Length 552 pages
Edition 1st Edition
Languages
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Author (1):
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Kieran Kavanagh Kieran Kavanagh
Author Profile Icon Kieran Kavanagh
Kieran Kavanagh
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Table of Contents (24) Chapters Close

Preface 1. Part 1:The Basics FREE CHAPTER
2. Chapter 1: AI/ML Concepts, Real-World Applications, and Challenges 3. Chapter 2: Understanding the ML Model Development Life Cycle 4. Chapter 3: AI/ML Tooling and the Google Cloud AI/ML Landscape 5. Part 2:Diving in and building AI/ML solutions
6. Chapter 4: Utilizing Google Cloud’s High-Level AI Services 7. Chapter 5: Building Custom ML Models on Google Cloud 8. Chapter 6: Diving Deeper – Preparing and Processing Data for AI/ML Workloads on Google Cloud 9. Chapter 7: Feature Engineering and Dimensionality Reduction 10. Chapter 8: Hyperparameters and Optimization 11. Chapter 9: Neural Networks and Deep Learning 12. Chapter 10: Deploying, Monitoring, and Scaling in Production 13. Chapter 11: Machine Learning Engineering and MLOps with Google Cloud 14. Chapter 12: Bias, Explainability, Fairness, and Lineage 15. Chapter 13: ML Governance and the Google Cloud Architecture Framework 16. Chapter 14: Additional AI/ML Tools, Frameworks, and Considerations 17. Part 3:Generative AI
18. Chapter 15: Introduction to Generative AI 19. Chapter 16: Advanced Generative AI Concepts and Use Cases 20. Chapter 17: Generative AI on Google Cloud 21. Chapter 18: Bringing It All Together: Building ML Solutions with Google Cloud and Vertex AI 22. Index 23. Other Books You May Enjoy

AI/ML Concepts, Real-World Applications, and Challenges

This chapter will introduce basic concepts that will be explored in more detail throughout the rest of the book. We understand that readers of this book may be starting from different stages in their artificial intelligence/machine learning (AI/ML) journey, whereby some readers may already be advanced practitioners who are familiar with running AI/ML workloads while others may be newer to AI/ML in general. For this reason, we will briefly describe important fundamental concepts as required throughout the book to ensure that all readers have a common baseline upon which to build their understanding of the topics we discuss. Readers who are newer to AI/ML will benefit from learning the important underlying concepts rather than diving straight into the deep end of each topic without a baseline context, and advanced practitioners should find them to be useful knowledge refreshers.

In this chapter, we’re going to cover the...

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