The document outlines the steps to deploy a machine learning model in Azure ML Service: 1. Create a deployment workspace separate from the training workspace. 2. Register the trained model to the container registry in the deployment workspace. 3. Create a Python scoring script to consume the model and score new data. 4. Create a YAML file specifying the Python dependencies and build a container image. 5. Deploy the model as a web service using the container image.

1. Data Wolf
Model Deployment in Azure ML Service
Score new data with the
deployed model
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Create a deployment workspace
Register model to the container
registry in the workspace
Create a model scoring script
Create a YAML file configuring
Python module dependencies
Create a container image
Deploy a model as a web service
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• Same as Machine Learning workspace
• Better to keep separate workspaces for Learning & Deployment
• Create in Portal or through azureml-SDK in Python (“Workspace.create” API)
• Test scoring with any REST API testing mechanism.
• Container image consist of 1. Model 2. Inference Engine 3. Scoring file 4.
Dependency file (YAML)
• ContainerImage.image_configuration creates configuration file
• Image.create creates the Docker image file.
• azureml.core.image contains Image & ContainerImage sub packages
• azureml.core.conda_dependencies api provides mechanism to create a conda
depenedency file, which can be written to a YAML file (.yml).
• Create a deployment configuration - AciWebservice.deploy_configuration
• Deploy the previously created image - Webservice.deploy_from_image
• APIs are part of “azureml.core.webservice”
• Model registry is same as container registry – single place to track all models in WS.
• Use “Model.register” API in azureml.core.model package.
• Register the pickle (.pkl) file created from the training experiment.
• Create a score.py that will consume the model
• Bring model from registry/WS using “Model.get_model_path” API
• Code logic to score for new data.
Natarajan Ganapathi Sep 2019