Journey to Become a Google Cloud Machine Learning Engineer: Build the mind and hand of a Google Certified ML professional

Prepare for the GCP ML certification exam along with exploring cloud computing and machine learning concepts and gaining Google Cloud ML skills

Key Features

• A comprehensive yet easy-to-follow Google Cloud machine learning study guide
• Explore full-spectrum and step-by-step practice examples to develop hands-on skills
• Read through and learn from in-depth discussions of Google ML certification exam questions

Book Description

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.

What you will learn

• Provision Google Cloud services related to data science and machine learning
• Program with the Python programming language and data science libraries
• Understand machine learning concepts and model development processes
• Explore deep learning concepts and neural networks
• Build, train, and deploy ML models with Google BigQuery ML, Keras, and Google Cloud Vertex AI
• Discover the Google Cloud ML Application Programming Interface (API)
• Prepare to achieve Google Cloud Professional Machine Learning Engineer certification

Who this book is for

Anyone from the cloud computing, data analytics, and machine learning domains, such as cloud engineers, data scientists, data engineers, ML practitioners, and engineers, will be able to acquire the knowledge and skills and achieve the Google Cloud professional ML Engineer certification with this study guide. Basic knowledge of Google Cloud Platform and Python programming is required to get the most out of this book.

Table of Contents

1. Comprehending Google Cloud Services
2. Mastering Python Programming
3. Preparing for ML Development
4. Developing and Deploying ML Models
5. Understanding Neural Networks and Deep Learning
6. Learning BQ/BQML, TensorFlow and Keras
7. Exploring Google Cloud Vertex AI
8. Discovering Google Cloud ML API
9. Using Google Cloud ML Best Practices
10. Achieving the GCP ML Certification
11. Appendix 1 - Practicing with Basic GCP Services
12. Appendix 2 - Practicing with Python Data Library
13. Appendix 3 - Practicing with ScikitLearn
14. Appendix 4 - Practicing with Vertex AI
15. Appendix 5 - Practicing with Google Cloud ML API

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.