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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 – predictions (Batch Prediction)

Batch prediction is used when you don’t require an immediate response and want to get predictions from the accumulated data via a single request. Follow these steps to perform batch prediction for the models we trained earlier:

  1. Go to Models from the left menu of the console.
  2. Click on the model you want to work with.
  3. Click on the version of the model you want to work with.
  4. From the top menu, click on BATCH PREDICT.
  5. Click on the blue CREATE BATCH PREDICTION button:

After clicking on CREATE BATCH PREDICTION, you need to define some parameters, such as the batch prediction’s name, source, output, and so on. Let’s analyze each of them:

  • Batch prediction name: Enter a name for the batch prediction.
  • Select source: Here, you need to specify the source of the value that will be used in batch prediction. You can source either the BigQuery table or the file...
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