Productionizing Machine Learning Pipelines with Databricks and Azure ML

Productionizing Machine Learning Pipelines with Databricks and Azure ML

Databricks via YouTube Direct link

Tensorflow Training

18 of 26

18 of 26

Tensorflow Training

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Classroom Contents

Productionizing Machine Learning Pipelines with Databricks and Azure ML

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  1. 1 Introduction
  2. 2 Local Environment Configuration
  3. 3 Virtual Environment Configuration
  4. 4 Poetry
  5. 5 Bundled Fault
  6. 6 Project Layout
  7. 7 Business Logic Code
  8. 8 Azure ML Workspace
  9. 9 Code Block
  10. 10 Authentication
  11. 11 Creating the Cluster
  12. 12 Running the Code
  13. 13 Attaching Databricks to Compute Target
  14. 14 Mounting Blob Store to Databricks
  15. 15 Setting up Databricks Connect
  16. 16 Running Databricks Connect
  17. 17 Data Set and Preprocessing
  18. 18 Tensorflow Training
  19. 19 Code Overview
  20. 20 ML Flow Source Code
  21. 21 Getting the Best Model
  22. 22 Registering the Model
  23. 23 Testing the Model
  24. 24 Azure ML Pipelines
  25. 25 Pipeline Infrastructure Code
  26. 26 Continuous Integration Phase

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