You're reading from Architecting Google Cloud Solutions Learn to design robust and future-proof solutions with Google Cloud technologies

Product type Paperback

Published in Apr 2021

Publisher Packt

ISBN-13 9781800563308

Length 472 pages

Edition 1st Edition

Tools

Google App Engine

Concepts

Cloud Computing

Author (1):

Victor Dantas

View More author details

Table of Contents (17) Chapters

Preface

1. Section 1: Introduction to Google Cloud

2. Chapter 1: An Introduction to Google Cloud for Architects FREE CHAPTER

3. Chapter 2: Mastering the Basics of Google Cloud

4. Section 2: Designing Great Solutions in Google Cloud

5. Chapter 3: Designing the Network

6. Chapter 4: Architecting Compute Infrastructure

7. Chapter 5: Architecting Storage and Data Infrastructure

8. Chapter 6: Configuring Services for Observability

9. Chapter 7: Designing for Security and Compliance

10. Section 3: Designing for the Modern Enterprise

11. Chapter 8: Approaching Big Data and Data Pipelines

12. Chapter 9: Jumping on the DevOps Bandwagon with Site Reliability Engineering (SRE)

13. Chapter 10: Re-Architecting with Microservices

14. Chapter 11: Applying Machine Learning and Artificial Intelligence

15. Chapter 12: Achieving Operational Excellence

16. Other Books You May Enjoy

Chapter 11: Applying Machine Learning and Artificial Intelligence

The subject of Artificial Intelligence (AI) is no longer exclusive to research and innovation. It's here and now. In fact, it's been around for quite some time. With the advancements in Machine Learning (ML) science and techniques, along with the increase in hardware capacity and data availability, the entry barrier has never been lower. Modern and data-driven enterprises can now leverage cloud-based AI to unlock insights from their data.

In this chapter, you will learn how to leverage Google Cloud Platform's (GCP's) AI services and prebuilt ML models. You will learn how these cloud-based solutions can add business value and discover real-world use cases. Then, we're going to explore how to build, train, and run custom models on GCP and even look at ways to do so without any coding or data science experience. Finally, you will learn how to apply MLOps to automate ML pipelines and productionize...