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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

ML workflow orchestration

As we discussed in Chapter 7, Exploring Google Cloud Vertex AI, Vertex AI Pipelines is a fully managed service that allows you to retrain your models as often as necessary so that you can adapt to changes and maintain performance over time. We recommend Vertex AI Pipelines for Cloud ML workflow orchestration.

If you’re using the Google TensorFlow framework, we recommend using TensorFlow Extended to define your pipeline and the operations for each step, then executing it on Vertex AI’s serverless pipeline system. TensorFlow provides pre-built components for common steps in the Vertex AI workflow, such as data ingestion, data validation, and training.

If you are using other frameworks, we recommend using Kubeflow Pipeline, which is very flexible and allows you to use simple code to construct pipelines. Kubeflow Pipeline also provides Google Cloud pipeline components such as Vertex AI AutoML.

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