#THIS CODE IS PROVIDED WITH ABSOLUTELY NO WARRANTY OR SUPPORT AND SHOULD ONLY SERVE AS A DEMO OF THE AVAILABLE FEATURES
This project demonstrates the use of Azure DevOps as the tool to work with ML Pipelines in Azure Databricks.-
Workflow Steps
- Data Scientist promotes model into Staging (e.g. as in the train_wine_model Notebook)
- Pipeline gets triggered (via Webhooks, manually or via API, as in the train_wine_model Notebook)
- Azure Dev Ops uploads deployment notebook (deploy_azure_ml_model) from git to a dedicated Test/QA region within the workspace via the Databricks workspace API
- Azure Dev Ops runs deploy notebook with creater job and run submit which does the following:
- Retrieves latest model staging from registry
- Deploys model as an Azure ML model and creates an image
- Deploys REST API for he model/image
- returns an the REST API deployment URL to Azure Dev Ops
- Azure Dev Ops uploads test notebook from git to a dedicated Test/QA region within the workspace
- Azure Dev Ops runs deploy notebook with run submit which does the following:
- Retrieves test data
- Invokes REST API
- If successful, DevOps will deploy the model into production using the mlFlow REST API# databricksMLOpsAzureDemo