Running MLflow, including example/test population process, from Docker container
MLflow is a wonderful and important tool in the daily machine learning and data science workflow, and even its public domain version can take us really far. For realistic use though, it needs to be set up with a full database system (such as PostgreSQL) used as its backend, and a network shared filesystem for its artifact store, and one may want some access gating to prevent just anyone in the company from accessing it.
The setup in my aganse/docker_mlflow_db repo provides a get-running-quickly Docker-compose configuration using containers for MLflow, PostgreSQL, and NGINX. Run MLflow's database in PostgreSQL, and put an NGINX reverse proxy in front of the MLflow website to allow some level of access restriction, while allowing one to very quickly put up the service on the spot (or take it down again for that matter).
MLflow is a wonderful and important tool in the daily machine learning and data science workflow, and even its public domain version can take us really far. For realistic use though, it needs to be set up with a full database system (such as PostgreSQL) used as its backend, and a network shared filesystem for its artifact store, and one may want some access gating to prevent just anyone in the company from accessing it.
The setup in my aganse/docker_mlflow_db repo provides a get-running-quickly Docker-compose configuration using containers for MLflow, PostgreSQL, and NGINX. Run MLflow's database in PostgreSQL, and put an NGINX reverse proxy in front of the MLflow website to allow some level of access restriction, while allowing one to very quickly put up the service on the spot (or take it down again for that matter).