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

Product type Paperback

Published in Jun 2024

Publisher Packt

ISBN-13 9781803245270

Length 552 pages

Edition 1st Edition

Languages

Gen

Tools

Google Cloud Platform

Concepts

Machine Learning

Author (1):

Kieran Kavanagh

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Table of Contents (24) Chapters

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

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Libraries

In this section, we describe the libraries we will use in this chapter, such as TensorFlow and Keras.

TensorFlow

TensorFlow is an open source software (OSS) library developed by the Google Brain team for ML and DNN research. However, it is also possible to run it on a wide range of systems, from mobile devices to multi-GPU setups, and it can have many applications beyond just ML. In this section, we will discuss some of its important aspects, such as the following:

Tensors, which are a generalization of vectors and matrices in multiple dimensions (we can think of them as multi-dimensional arrays or lists). They are the basic building blocks in TensorFlow.
Data-flow graphs (DFGs), in which nodes in the graph represent mathematical operations and edges represent the data (tensors) transmitted between these nodes. This approach enables parallel computation across multiple devices, making TensorFlow suitable for training large NNs.
Multiple options for model...