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

Product type Paperback

Published in Sep 2022

Publisher Packt

ISBN-13 9781803233727

Length 330 pages

Edition 1st Edition

Languages

Python

Tools

BigQuery

Concepts

Machine Learning

Author (1):

Dr. Logan Song

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Table of Contents (23) Chapters

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

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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 storage and database service spectrum

Previously, we examined the GCS service and created our storage bucket in the cloud, as well as the persistent disks and Filestore instances for our cloud VM instances. Now, let’s look at the whole GCP storage and database service spectrum, which includes Cloud Storage, Cloud SQL, Cloud Spanner, Cloud Firestore, Bigtable, and BigQuery, as shown in the following diagram:

Figure 1.5 – GCP storage and database services

Here, Cloud Storage stores objects, Cloud SQL and Cloud Spanner are the relational databases, Cloud Firestore and Bigtable are NoSQL databases.BigQuery is a data warehouse as well as a bigdata analytical/visualization tool. We will discuss BigQuery in the GCP big data and analytics services section.

GCP storage

We have already discussed GCP storage, including Google Cloud Storage (GCS), persistent disks, and Filestore. GCS is a common choice for GCP ML jobs to store their training data, models, checkpoints, and logs. In the next few sections, we will discuss more GCP storage databases and services.

Google Cloud SQL

Cloud SQL is a fully managed GCP relational database service for MySQL, PostgreSQL, and SQL Server. With Cloud SQL, you run the same relational databases you are familiar with on-premises, without the hassle of self-management, such as backup and restore, high availability, and more. As a managed service, it is the responsibility of Google to manage the database backups, export and import, ensure high availability and failover, perform patch maintenance and updates, and perform monitoring and logging.

Google Cloud Spanner

Google Cloud Spanner is a GCP fully managed relational database with unlimited global scale, strong consistency, and up to 99.999% availability. Like a relational database, Cloud Spanner has schemas, SQL, and strong consistency. Also, like a non-relational database, Cloud Spanner offers high availability, horizontal scalability, and configurable replications. Cloud Spanner has been used for mission-critical business use cases, such as online trading systems for transactions and financial management.

Cloud Firestore

Cloud Firestore is a fast, fully managed, serverless, cloud-native NoSQL document database. Cloud Firestore supports ACID transactions and allows you to run sophisticated queries against NoSQL data without performance degradation. It stores, syncs and query data for mobile apps and web apps at global scale. Firestore integrates with Firebase and other GCP services seamlessly and thus accelerates serverless application development.

Google Cloud Bigtable

Cloud Bigtable is Google’s fully managed NoSQL big data database service. Bigtable stores data in tables that are sorted using key/value maps. Bigtable can store trillions of rows and millions of columns, enabling applications to store petabytes of data. Bigtable provides extreme scalability and automatically handles database tasks such as restarts, upgrades, and replication. Bigtable is ideal for storing very large amounts of semi-structured or non-structured data, with sub-10 milliseconds latency and extremely high read and write throughput. Many of Google’s core products such as Search, Analytics, Maps, and Gmail use Cloud Bigtable.