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Professional Cloud Architect –  Google Cloud Certification Guide

You're reading from   Professional Cloud Architect – Google Cloud Certification Guide A handy guide to designing, developing, and managing enterprise-grade GCP cloud solutions

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Product type Paperback
Published in Oct 2019
Publisher Packt
ISBN-13 9781838555276
Length 520 pages
Edition 1st Edition
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Authors (2):
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Brian Gerrard Brian Gerrard
Author Profile Icon Brian Gerrard
Brian Gerrard
Konrad CÅ‚apa Konrad CÅ‚apa
Author Profile Icon Konrad CÅ‚apa
Konrad CÅ‚apa
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Toc

Table of Contents (26) Chapters Close

Preface 1. Section 1: Introduction to GCP FREE CHAPTER
2. GCP Cloud Architect Professional 3. Getting Started with Google Cloud Platform 4. Google Cloud Platform Core Services 5. Section 2: Managing, Designing, and Planning a Cloud Solution Architecture
6. Working with Google Compute Engine 7. Managing Kubernetes Clusters with Google Kubernetes Engine 8. Exploring Google App Engine as a Compute Option 9. Running Serverless Functions with Google Cloud Functions 10. Networking Options in GCP 11. Exploring Storage Options in GCP - Part 1 12. Exploring Storage Options in GCP - Part 2 13. Analyzing Big Data Options 14. Putting Machine Learning to Work 15. Section 3: Designing for Security and Compliance
16. Security and Compliance 17. Section 4: Managing Implementation
18. Google Cloud Management Options 19. Section 5: Ensuring Solution and Operations Reliability
20. Monitoring Your Infrastructure 21. Section 6: Exam Focus
22. Case Studies 23. Test Your Knowledge 24. Assessments 25. Other Books You May Enjoy

Cloud ML Engine

Cloud ML is a managed service that allows you to train and host ML models without worrying about the underlying infrastructure. It provisions all of the requisite resources.

You can accelerate the learning process since a range of CPU, GPU, and TPU nodes are supported. It works with multiple frameworks, but the most popular is TensorFlow. As TensorFlow is open source, it allows for portability. Models can be trained locally on limited data and then sent to GCP to train at scale. Cloud ML integrates with other GCP services, such as Cloud Storage for data storage and Cloud Dataflow for data processing.

Using ML Engine

The following diagram should give you an idea of where ML Engine fits into the process of developing...

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