Book Image

Journey to Become a Google Cloud Machine Learning Engineer

By : Dr. Logan Song
Book Image

Journey to Become a Google Cloud Machine Learning Engineer

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)
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
Appendix 2: Practicing Using the Python Data Libraries

Vertex AI – predictions (Vertex AI Endpoint)

In this section, we are going to deploy our model via Vertex AI Endpoint. There are two ways to deploy a model:

  • From Models
  • From Endpoints

Let’s look at these options in detail.

Deploying the model via Models

Go to the Models section from the left menu, select the model you want to deploy, and select the version you want to deploy (remember, we have only one version since we have built/trained a brand-new model). Then, at the top of the page, click on DEPLOY & TEST:

After clicking on DEPLOY & TEST, you will be taken to the next page. Here, click on the blue DEPLOY TO ENDPOINT button:

Deploying the model via Endpoints

Go to the Endpoints section from the left menu. By doing so, you will be navigated to a pop-up page where you need to define your endpoint:

On the next page, you need to specify which model you want...