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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

GCP ML options

With GCP, you have multiple options when it comes to leveraging ML. Which one you choose largely depends on your use case and how knowledgeable you are on the topic. The following options are available:

  • TensorFlow (for a data scientist): This is an option for those who want to work with ML from scratch. It is a software library that's developed and open-sourced by Google. There are more libraries on the market, but this one is the most popular and is used by other cloud providers for their managed ML services.
  • Vertex AI (for a data scientist): This is an option for those who want to train their own models but who use Google for training and predictions. It is a managed TensorFlow/Kubeflow service that offloads all infrastructure and software bits from users.
  • Pretrained ML models (for a developer): This is an option for those who want to leverage ML without having any knowledge of it. It allows Google-developed models to be used to perform predictions...
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