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Professional Cloud Architect Google Cloud Certification Guide

You're reading from   Professional Cloud Architect Google Cloud Certification Guide Build a solid foundation in Google Cloud Platform to achieve the most lucrative IT certification

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Product type Paperback
Published in Dec 2021
Publisher Packt
ISBN-13 9781801812290
Length 664 pages
Edition 2nd Edition
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Authors (2):
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Konrad Cłapa Konrad Cłapa
Author Profile Icon Konrad Cłapa
Konrad Cłapa
Brian Gerrard Brian Gerrard
Author Profile Icon Brian Gerrard
Brian Gerrard
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Toc

Table of Contents (25) Chapters Close

Preface 1. Section 1: Introduction to GCP
2. Chapter 1: GCP Cloud Architect Professional FREE CHAPTER 3. Chapter 2: Getting Started with Google Cloud Platform 4. Chapter 3: Google Cloud Platform Core Services 5. Section 2: Manage, Design, and Plan a Cloud Solution Architecture
6. Chapter 4: Working with Google Compute Engine 7. Chapter 5: Exploring Google App Engine as a Compute Option 8. Chapter 6: Managing Kubernetes Clusters with Google Kubernetes Engine 9. Chapter 7: Deploying Cloud-Native Workloads with Cloud Run 10. Chapter 8: Managing Cloud-Native Workloads with Anthos 11. Chapter 9: Running Serverless Functions with Google Cloud Functions 12. Chapter 10: Networking Options in GCP 13. Chapter 11: Exploring Storage and Database Options in GCP – Part 1 14. Chapter 12: Exploring Storage and Database Options in GCP – Part 2 15. Chapter 13: Analyzing Big Data Options 16. Chapter 14: Putting Machine Learning to Work 17. Section 3: Secure, Manage and Monitor a Google Cloud Solution
18. Chapter 15: Security and Compliance 19. Chapter 16: Google Cloud Management Options 20. Chapter 17: Monitoring Your Infrastructure 21. Section 4: Exam Focus
22. Chapter 18: Case Studies 23. Chapter 19: Test Your Knowledge 24. Other Books You May Enjoy

AutoML

AutoML comes into play when pretrained models are not fit for purpose. As an example, the Vision API can recognize a sofa, but what if we want to recognize a particular sofa that our company produces? The Vision API cannot do that for us.

In such a case, we need to use AutoML or train our own model. As you have probably already guessed, the former is a much easier method. AutoML takes datasets from you, trains and deploys the model, and then serves it through the REST API. This sounds a little bit like magic, right? Take a look at the following diagram:

Figure 14.20 – AutoML

Note that there are five services available that allow you to train your custom model:

  • AutoML Vision: This classifies your images according to your own defined labels.
  • AutoML Translation: This performs translation queries, returning results specific to your domain.
  • AutoML Natural Language: This classifies English-language content into a custom set of categories...
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