You can use Azure Machine Learning to package, debug, validate and deploy inference containers to a variety of compute targets. This process is known as "MLOps" (ML operationalization). For more information please check out this article: https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-deploy-and-where

Get Started

To begin, you will need an ML workspace. For more information please check out this article: https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-workspace

Deploy to the cloud

You can deploy to the cloud using the Azure ML CLI or the Azure ML SDK.

Deploy with the CLI

az extension add -n azure-cli-ml
az ml folder attach -w myworkspace -g myresourcegroup
az ml model register -n sklearn_regression_model.pkl -p sklearn_regression_model.pkl -t model.json
az ml model deploy -n acicicd -f model.json --ic inferenceConfig.yml --dc deploymentConfig.yml

Here is an Azure DevOps Pipelines model deployment example

Deploy from a notebook

Notebook example: model-register-and-deploy.