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  • Book Overview & Buying Journey to Become a Google Cloud Machine Learning Engineer
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Journey to Become a Google Cloud Machine Learning Engineer

Journey to Become a Google Cloud Machine Learning Engineer

By : Dr. Logan Song
5 (61)
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Journey to Become a Google Cloud Machine Learning Engineer

Journey to Become a Google Cloud Machine Learning Engineer

5 (61)
By: Dr. Logan Song

Overview of this book

This book aims to provide a study guide to learn and master machine learning in Google Cloud: to build a broad and strong knowledge base, train hands-on skills, and get certified as a Google Cloud Machine Learning Engineer. The book is for someone who has the basic Google Cloud Platform (GCP) knowledge and skills, and basic Python programming skills, and wants to learn machine learning in GCP to take their next step toward becoming a Google Cloud Certified Machine Learning professional. The book starts by laying the foundations of Google Cloud Platform and Python programming, followed the by building blocks of machine learning, then focusing on machine learning in Google Cloud, and finally ends the studying for the Google Cloud Machine Learning certification by integrating all the knowledge and skills together. The book is based on the graduate courses the author has been teaching at the University of Texas at Dallas. When going through the chapters, the reader is expected to study the concepts, complete the exercises, understand and practice the labs in the appendices, and study each exam question thoroughly. Then, at the end of the learning journey, you can expect to harvest the knowledge, skills, and a certificate.
Table of Contents (23 chapters)
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1
Part 1: Starting with GCP and Python
4
Part 2: Introducing Machine Learning
8
Part 3: Mastering ML in GCP
13
Part 4: Accomplishing GCP ML Certification
15
Part 5: Appendices
2
Appendix 2: Practicing Using the Python Data Libraries

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

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.

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