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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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Toc

Table of Contents (24) Chapters Close

Preface 1. Part 1:The Basics
2. Chapter 1: AI/ML Concepts, Real-World Applications, and Challenges FREE CHAPTER 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

Part 3:Generative AI

In this part, we begin our journey into the exciting universe of generative AI, which is currently taking the world by storm. This part of the book first introduces the fundamental concepts of generative AI, and we compare and contrast it against “traditional AI.” We then move on to cover more advanced topics and learn how to implement those concepts in Google Cloud. The final chapter of this part then focuses on bringing together the main concepts that we’ve learned throughout the book, and lays out instructions to build AI/ML and generative AI solutions using a combination of various Google Cloud products and services.

This part contains the following chapters:

  • Chapter 15, Introduction to Generative AI
  • Chapter 16, Advanced Generative AI Concepts and Use Cases
  • Chapter 17, Generative AI on Google Cloud
  • Chapter 18, Bringing It All Together: Building ML Solutions with Google Cloud and Vertex AI
...
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