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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

Index

As this ebook edition doesn't have fixed pagination, the page numbers below are hyperlinked for reference only, based on the printed edition of this book.

Symbols

Local Interpretable Model-agnostic Explanations (LIME) 292

software development life cycle (SDLC) 31

A

A/B testing 266-268

Accelerated Linear Algebra (XLA) 379

action potential 237

activation function 237, 244

hyperbolic tangent (tanh) activation function 246

Leaky ReLU activation function 247, 248

linear activation function 244

Rectified Linear Unit (ReLU) activation function 246, 247

sigmoid activation function 245

softmax activation function 248, 249

adapter layers 430, 441

adapter modules 441

adapter tuning 430, 441

Adaptive Gradient Algorithm (Adagrad) 244

Adaptive Moment Estimation (Adam) 244

agent 16, 437

AI Fairness 360 (AIF360) 323

AI Hypercomputer 379

algorithmic bias 303

AlloyDB AI Vector Search 473

All-Reduce...

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