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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

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Product type Paperback
Published in Sep 2022
Publisher Packt
ISBN-13 9781803233727
Length 330 pages
Edition 1st Edition
Languages
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Author (1):
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Dr. Logan Song Dr. Logan Song
Author Profile Icon Dr. Logan Song
Dr. Logan Song
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Table of Contents (23) Chapters Close

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 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

Vertex AI experiments and TensorBoard

TensorBoard is a Google open source project for machine learning experiment visualization. Vertex AI experiments are an implementation of TensorBoard. With Vertex AI experiments, users can create TensorBoard instances and upload TensorBoard logs generated from Vertex AI Models to run experiments – visual representations of a variety of metrics, such as loss function and accuracy over different model parameters at different running times. Figure 7.4 shows a sample workflow for Vertex AI experiments and TensorBoard:

Figure 7.3 – Vertex AI experiments and TensorBoard

These TensorBoard visualizations are available via a web application that can be shared with other users by setting up GCP IAM permissions. With Vertex AI experiments, you can configure the following options:

  • Manage TensorBoard Instances: Users can create, update, or delete TensorBoard instances; instances are used for experiments.
  • Create...
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