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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

Understanding the GCP global infrastructure

Google is one of the biggest cloud service providers in the world. With the physical computing infrastructures such as computers, hard disk drives, routers, and switches in Google’s worldwide data centers, which are connected by Google’s global backbone network, Google provides a full spectrum of cloud services in GCP, including compute, network, database, security, and advanced services such as big data, machine learning (ML), and many, many more.

Within Google’s global cloud infrastructure, there are many data center groups. Each data center group is called a GCP region. These regions are located worldwide, in Asia, Australia, Europe, North America, and South America. These regions are connected by Google’s global backbone network for performance optimization and resiliency. Each GCP region is a collection of zones that are isolated from each other. Each zone has one or more data centers and is identified by a name that combines a letter identifier with the region’s name. For example, zone US-Central1-a is a zone in the US-Central1 region, which is physically located in Council Bluffs, Iowa, the United State of America. In the GCP global infrastructure, there are also many edge locations or points of presence (POPs) where Google’s global networks connect to the internet. More details about GCP regions, zones, and edge locations can be found at https://cloud.google.com/about/locations.

GCP provides on-demand cloud resources at a global scale. These resources can be used together to build solutions that help meet business goals and satisfy technology requirements. For example, if a company needs 1,000 TB of storage in Tokyo, its IT professional can log into their GCP account console and provision the storage in the Asia-northeast1 region at any time. Similarly, a 3,000 TB database can be provisioned in Sydney and a 4,000-node cluster in Frankfurt at any time, with just a few clicks. And finally, if a company wants to set up a global website, such as zeebestbuy.com, with the lowest latencies for their global users, they can build three web servers in the global regions of London, Virginia, and Singapore, and utilize Google’s global DNS service to distribute the web traffic along these three web servers. Depending on the user’s web browser location, DNS will route the traffic to the nearest web server.

You have been reading a chapter from
Journey to Become a Google Cloud Machine Learning Engineer
Published in: Sep 2022
Publisher: Packt
ISBN-13: 9781803233727
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