This repo maps out an MLOps infrastructure utilizing the following technologies:

• Jupyter notebooks for hosting and managing data science and machine learning activities
• MLFlow for tracking experiments, experiment runs, experiment metadata (input parameters and output metrics), experiment artifacts (curated datasets, etc.) and trained models
• Azure blob storage as artifact backing store
• SQL Server as experiment metadata backing store

• All services run as Docker containers on your local workstation

• Docker Desktop
• Bash or other Unix-style shell (WSL2 on Windows)

• clone this repo

• open a shell in the repo folder root

• run start.local.sh

  > ./start.local.sh

• wait for all containers to build and start (may take a few minutes)

• follow the remaining common instructions

• Jupyter and MLFlow servers run as Docker containers on your local workstation
• Cloud-based Azure storage
• Cloud-based SQL storage

• Docker Desktop
• Bash or other Unix-style shell (WSL2 on Windows)
• A new or existing Azure storage account (how to create a new one)
• Add a new blob container to the storage account called artifacts
• A new Azure SQL Database called mlflowmetadata

• clone this repo

• open a shell in the repo folder root

• edit start.cloud.sh and ensure the AZURE_STORAGE_CONNECTION_STRING and MLFLOW_DEFAULT_ARTIFACT_ROOT variables point to your storage account/container, and the MLFLOW_BACKEND_STORE_URI variable points to your SQL database

• run start.cloud.sh

  > ./start.cloud.sh

• wait for all containers to build and start (may take a few minutes)

• follow the remaining common instructions

• All services run in Azure cloud
• Enables multi-user collaboration and durable storage of metadata and artifacts

• TBD

• open JupyterLab by navigating to http://localhost:8888/?token=dtic
• open the train_and_serve_model.ipynb notebook and run all cells, in order... this will train and deploy a sample ML model to a local REST endpoint
• open another browser tab and navigate to the MLFlow UI at http://localhost:5000
• view the details of run1 under experiment1
• go back to Jupyter and open invoke_model_endpoint.ipynb to invoke the model REST endpoint