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