This repository provides necessary artefacts to quicky and easily deploy an MLflow Tracking server on Azure.

MLflow is an open source platform for managing the end-to-end machine learning lifecycle.

• az cli 2.0+

1. Ensure you are logged-in in the azure cli.
   • Run az login to login.
   • Run az account set -s <SUBSCRIPTION_ID> to set target azure subscription.
2. Ensure you are in the deploy-aci folder.
3. Open deploy-aci.sh and inspect/change top parameters, if necessary.
4. Run ./deploy-aci.sh
5. Validate deployment by navigating to the ACI IP:port (default: 5000). NOTE, that it takes a few moments for the server to startup.
   • You can retrieve IP and port of the deployed Tracking Server on ACI by running:
   • az container show --name <ACI_NAME> --resource-group <ACI_RESOURCE_GROUP> --output table

Documentaion for mlflow repository and scripts

• Mlflow server deployed on a container instance with:
  • storage container - with blob and file store
  • docker image for mlflow server
• Version 1.8.0 of mlflow
• Models saved to blob storage and meta data saved to SQLlite db inside the container

Build image

docker build --tag jaredmagrath/mlflowserver-azure:1.8.2 .\mlflow-tracking-docker\

Push to dockerhub

docker push jaredmagrath/mlflowserver-azure:1.8.2