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Journey to Become a Google Cloud Machine Learning Engineer

You're reading from   Journey to Become a Google Cloud Machine Learning Engineer Build the mind and hand of a Google Certified ML professional

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Product type Paperback
Published in Sep 2022
Publisher Packt
ISBN-13 9781803233727
Length 330 pages
Edition 1st Edition
Languages
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Author (1):
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Dr. Logan Song Dr. Logan Song
Author Profile Icon Dr. Logan Song
Dr. Logan Song
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Table of Contents (23) Chapters Close

Preface 1. Part 1: Starting with GCP and Python
2. Chapter 1: Comprehending Google Cloud Services FREE CHAPTER 3. Chapter 2: Mastering Python Programming 4. Part 2: Introducing Machine Learning
5. Chapter 3: Preparing for ML Development 6. Chapter 4: Developing and Deploying ML Models 7. Chapter 5: Understanding Neural Networks and Deep Learning 8. Part 3: Mastering ML in GCP
9. Chapter 6: Learning BQ/BQML, TensorFlow, and Keras 10. Chapter 7: Exploring Google Cloud Vertex AI 11. Chapter 8: Discovering Google Cloud ML API 12. Chapter 9: Using Google Cloud ML Best Practices 13. Part 4: Accomplishing GCP ML Certification
14. Chapter 10: Achieving the GCP ML Certification 15. Part 5: Appendices
16. Index 17. Other Books You May Enjoy Appendix 1: Practicing with Basic GCP Services 1. Appendix 2: Practicing Using the Python Data Libraries 2. Appendix 3: Practicing with Scikit-Learn 3. Appendix 4: Practicing with Google Vertex AI 4. Appendix 5: Practicing with Google Cloud ML API

GCP organization structure

Before we discuss the GCP cloud services further, we need to spend some time talking about the GCP organization structure, which is quite different from that of the Amazon Web Services (AWS) cloud and the Microsoft Azure cloud.

The GCP resource hierarchy

As shown in the following diagram, within a GCP cloud domain, at the top is the GCP organization, followed by folders, then projects. As a common practice, we can map a company’s organizational hierarchy to a GCP structure: a company maps to a GCP organization, its departments (sales, engineering, and more) are mapped to folders, and the functional projects from the departments are mapped to projects under the folders. Cloud resources such as VMs, databases (DBs), and so on are under the projects.

In a GCP organization hierarchy, each project is a separate compartment, and each resource belongs to exactly one project. Projects can have multiple owners and users. They are managed and billed separately, although multiple projects may be associated with the same billing account:

Figure 1.1 – Sample GCP organization structure

Figure 1.1 – Sample GCP organization structure

In the preceding diagram, there are two organizations: one for production and one for testing (sandbox). Under each organization, there are multiple layers of folders (note that the number of folder layers and the number of folders at each layer may be limited), and under each folder, there are multiple projects, each of which contains multiple resources.

GCP projects

GCP projects are the logical separations of GCP resources. Projects are used to fully isolate resources based on Google Cloud’s Identity and Access Management (IAM) permissions:

  • Billing isolation: Use different projects to separate spending units
  • Quotas and limits: Set at the project level and separated by workloads
  • Administrative complexity: Set at the project level for access separation
  • Blast radius: Misconfiguration issues are limited within a project
  • Separation of duties: Business units and data sensitivity are separate

In summary, the GCP organization structure provides a hierarchy for managing Google Cloud resources, with projects being the logical isolation and separation. In the next section, we will discuss resource permissions within the GCP organization by looking at IAM.

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Journey to Become a Google Cloud Machine Learning Engineer
Published in: Sep 2022
Publisher: Packt
ISBN-13: 9781803233727
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