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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

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

View More author details

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

Why subscribe?

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

Learning BQ/BQML, TensorFlow, and Keras

After building the GCP and Python foundations in part one and understanding the ML concepts and development process in part two, we are now entering part three of the book: Mastering ML in GCP. We will start by learning how Google does ML for structured data and the Google ML frameworks TensorFlow and Keras. In this chapter, we will cover the following topics:

GCP BQ
GCP BQML
Introduction to TensorFlow
Introduction to Keras

In recent years, relational databases have been widely used in many enterprises, and thus structural data is a big portion of the big data available for many businesses. Google’s BQ and BQML play a big role in relational/structural data processing and analytics.

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Authors (1)

Dr. Logan Song

Dr. Logan Song

Dr. Logan Song is the enterprise cloud director and chief cloud architect at Dito. With 25+ years of professional experience, Dr. Song is highly skilled in enterprise information technologies, specializing in cloud computing and machine learning. He is a Google Cloud-certified professional solution architect and machine learning engineer, an AWS-certified professional solution architect and machine learning specialist, and a Microsoft-certified Azure solution architect expert. Dr. Song holds a Ph.D. in industrial engineering, an MS in computer science, and an ME in management engineering. Currently, he is also an adjunct professor at the University of Texas at Dallas, teaching cloud computing and machine learning courses.

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