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 data labeling and datasets

Datasets play such a significant role in the machine learning process that the quality of datasets has a huge impact on the ML model performance. As we discussed in Chapter 4, Developing and Deploying ML Models, data preparation is the first and most important step in any machine learning process.

Vertex AI Data labeling is a Google Cloud service that lets end users work with human workers to review and label datasets uploaded by users. After the datasets are labeled, they can be used to train machine learning models. The human workers are employed by Google, and the users will need to provide the dataset, the labels, and instructions to the human workers for labeling.

End users can also upload labeled datasets directly. Vertex AI datasets are part of a Google Cloud service that provides users with the ability to upload data of varying types for the purpose of building, training, and validating machine learning models. Currently, Vertex AI...