Azure Machine Learning is a cloud-based environment you can use to train, deploy, automate, manage, and track machine learning models and data science workflows. This deployment template takes an Infrastructure as Code approach with DevOps principles of continuous integration (CI) and continuous delivery (CD).
The template contains code and DevOps pipeline definitions to automate the deployment of an Azure Machine Learning Workspace along with associated resources and Azure Kubernetes Service as in inference cluster (when deploying machine learning models as web services). Collectivley, these form the basis of a the data science platform. The deployment template also includes the creation of a compute cluster and a tabular datasets.
- Azure subscription (contributor or owner)
- Azure DevOps project
- GitHub account
Follow the instructions in the getting started doc to deploy this solution in your own Azure subscription. You can find the details of the files and folders in the repository here.
Note: the dataset used in this deployment template is the Cardiovascular Disease dataset available on Kaggle.
Check out these related projects:
- AML Real-Time Scoring Deployment Template - automated end-to-end deployment of machine learning models as a web service for real-time inferencing
- AML Batch Scoring Deployment Template - automated end-to-end deployment of machine learning models as an AML Pipeline for batch inferencing