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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

Prerequisites

The steps in this section need to be completed before we can perform the primary activities in this chapter.

Enabling the Artifact Registry API

We’re going to create Docker images in order to run our custom code in conjunction with the Google Cloud Vertex AI Vizier service. The Google Cloud Artifact Registry is a fully managed artifact repository that we can use for storing our container images. It can be seen as the next generation of the Google Cloud Container Registry (GCR) that can be used to store artifacts such as Java JAR files, Node.js modules, Python wheels, Go modules, Maven artifacts, and npm packages (in addition to Docker images, which were already supported in GCR).

To enable the Artifact Registry API, perform the following steps:

  1. In the Google Cloud console, navigate to Google Cloud services menuAPIs & ServicesLibrary
  2. Search for Artifact Registry in the search box.
  3. Select the API in the list of results...
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