part_2.md

Part 2: Use cloud scale compute to run, deploy and manage ML experiment with Azure ML

Pre-requisites

• Complete Part 0, Part 1
• Run each module feature_engineering, ml_training and evaluating successfully in local mode
• Have Azure ML workspace setup with a Compute Cluster named cpu-cluster

Summary

After successfully restructuring the jupyter notebook and run modules locally, your team wants to leverage Azure cloud to run the experiment at scale. They also want to take advantage of experiment tracking and model management capabilities in Azure ML to keep track of experiment. Finally, the team wants to deploy the model as a rest endpoint for real time inferencing and experience the option of deploying it as batch inferencing. To accomplish these goals, you will perform the following:

• Run feature_engineering module as a job in Azure AML
• Run ml_training module as a job in Azure ML and observe the experiment metrics
• Run evaluating module as a job in Azure ML and observe how the model can be registered to Azure ML model's repo
• Run the three modules together as a pipeline
• Deploy and test the produced ML model as an API using Azure Managed Online Endpoint

Steps

1. Go to the workshop folder.

   Action Item: Run the following code snippet.

   cd src/workshop

2. Set defaults values to configure your resource group and workspace.

   Action Item: Run the following code snippet.

   az configure --defaults group=YOUR_RESOURCE_GROUP workspace=YOUR_WORKSPACE

3. Run the feature_engineering.py module under the data_engineering folder by following the steps below:

   Action Items:

   • Run the following code snippet:

     az ml job create -f core/data_engineering/feature_engineering.yml 

   • Go to Azure ML Studio and locate the run detail for this experiment.

4. Run the ml_training.py module under the training folder by following the steps below:

   Action Items:

   • Run the following code snippet:

     az ml job create -f core/training/ml_training.yml 

   • Go to Azure ML Studio and locate the run detail for this experiment.

5. Run the ml_evaluating.py module under the evaluating folder by following the steps below:

   Action Items:

   • Run the following code snippet:

     az ml job create -f core/evaluating/ml_evaluating.yml 

   • Go to Azure ML Studio and locate the run detail for this experiment. Observe the ML metrics and how the model was logged to Azure ML's model registry.

6. Create a pipeline that runs the feature_engineering, training and evaluation in one workflow.

   Action Items: Run the pipeline, by running the following code snippet.

     az ml job create -f core/pipelines/training_pipeline.yml 

   • Go to the run detail at Azure ML studio and observe the relationship graph among the modules. (See chart below as well.)

7. Discuss this question: Why should we run the modules both individually and together in a pipeline?

8. Deploy to Azure ML Managed Online Endpoint by following the steps below:

   Action Items:

   • Update the endpoint.yml file and deployment.yml by updating the name of the endpoint (should be a unique name)
   • Create your endpoint

     az ml online-endpoint create --file core/scoring/endpoint.yml 

   • Create a green deployment

     az ml online-deployment create --file core/scoring/deployment.yml 

   • Test the deployed service with mock-up data from scoring_test_request.json

     az ml online-endpoint invoke -n YOUR_ENDPOINT_NAME --deployment green --request-file core/scoring/scoring_test_request.json 

   • Observe the returned scores from the endpoint evaluation.

The entire training pipeline is illustrated with this diagram

Success criteria

• Run the modules individually in Azure
• Capture metrics and models in ml_training and ml_evaluating modules
• Run three modules together in a pipeline
• Model is deployed successfully to managed endpoint.
• Testing is successful

Reference materials

• Azure ML CLI v2 tutorial
• Azure ML CLI single job examples
• Azure ML CLI pipeline examples
• Deploy to managed online endpoint
• Deploy to batch endpoint

Go to Part 3