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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

Vertex AI – Feature Store

Feature Store in Vertex AI is where you can create entities and features and add values that can be used later as needed. In this demo, we are going to explore creating a Feature Store with entities and features in the Jupyter notebook by running some Python scripts. Before diving into the notebook, let’s create a Feature Store and entities via the Google Cloud console. From the left menu of the console, click on Features:

Since we haven’t created any Feature Store(s) yet, this section will be empty. To create a new entity, click on CREATE ENTITY TYPE, which is located at the top of the page:

From the popup menu, enter all the necessary information and click the CREATE button, as shown here:

Next, we are going to create a new notebook and clone a repository from GitHub (https://github.com/GoogleCloudPlatform/vertex-ai-samples). After creating a notebook...

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