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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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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

Fundamental concepts in this chapter

In this book, we aim to provide you with knowledge not only regarding how to use the relevant Google Cloud services for various types of workloads but also regarding important industry concepts that relate to each of the relevant technologies. In this section, we briefly cover concepts that provide additional context for this chapter’s learning activities.

Ingesting data into Google Cloud

In previous chapters, you already performed steps to upload data to GCS and Google Cloud BigQuery. In addition to performing bulk uploads to GCS and BigQuery, it’s also possible to stream data into those services. You will see this in action in this chapter.

This section provides a more holistic overview of Google Cloud’s data ingestion options. We will only cover Google Cloud’s services here, but there are also countless options of third-party database and data management services that you can run on Google Cloud, such as MongoDB...

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