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 Workbench and notebooks

The Vertex AI Workbench service provides a single development platform for the entire data science workflow; you can use it to launch Cloud VM instances/notebooks to query and explore data and to develop and train a model for deployment.

As we explained earlier in the Preparing the platform section, Jupyter Notebook is a widely used platform for ML model development. Vertex AI Workbench provides two Jupyter-Notebook-based options for your data scientists, managed notebooks and user-managed notebooks:

  • Managed notebooks are Google-managed, Jupyter-based, scalable, enterprise-ready compute instances that help you set up and work in an end-to-end ML production environment.
  • User-managed notebooks are heavily customizable instances and are thus fitting for users who need a lot of control over their environment. With a user-managed notebook instance, you have a suite of deep learning packages pre-installed, including TensorFlow and PyTorch...