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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

Exploring, visualizing, and preparing data

Use case: We’re planning a trip to New York City, and we want to get an idea of what the best accommodation options would be. Rather than perusing through and evaluating lots of individual Airbnb postings, we’re going to download lots of the reviews and do some bulk data analysis and data processing to get some insights.

We can use the Vertex AI Workbench notebook that we created in Chapter 5 for this purpose. Please open JupyterLab on that notebook instance. In the directory explorer on the left side of the screen, navigate to the Chapter-6 directory and open the Chapter-6-Airbnb.ipynb notebook. You can choose Python (Local) as the kernel. As you did in Chapter 5, run each cell in the notebook by selecting the cell and pressing Shift + Enter on your keyboard.

In the notebook, we use markdown cells to describe each step in detail so that you can understand each step in the process. We use libraries such as pandas, matplotlib...

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