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 – training

Now that you have a labeled dataset, it is ready for model training. Vertex AI provides different methods for training your model:

  • AutoML
  • Custom training (advanced)

To start AutoML training, in the Vertex AI console, click on Training and then the CREATE button, which is located at the top of the page (in our case, we are going to perform AutoML training using the dataset of MRI images that we created in the previous section):

On the page that appears, you need to define some specifications for the model you are trying to train:

  • Select Dataset: Here, you will be able to see all the datasets you created previously.
  • Annotation Set: Labels are saved in collections called annotations. You can change annotation sets to apply a different group of labels to the same dataset.

On the Training method page, select AutoML (this will be selected by default). Then, click CONTINUE:

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