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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 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

Part 2:Diving in and building AI/ML solutions

At this point, we have equipped ourselves with the prerequisite knowledge to start building AI/ML solutions on Google Cloud. Therefore, this part focuses on actually building those solutions. We begin this part by exploring Google Cloud’s high-level AI services. These are the services you can use without requiring any expertise in AI/ML. We then move on to building custom ML models on Google Cloud. After building your first custom model, the remaining chapters in this part focus on diving deep into each step in the typical AI/ML project, such as data preparation, feature engineering, hyperparameter optimization, and deploying, monitoring, and scaling in production. We then dive into the concept of MLOps to automate all of the steps in the machine learning model development lifecycle. Next, we go beyond the technology realm and incorporate broader business and societal concepts that apply to AI/ML models, such as bias, explainability...

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