This post provides an easy-to-follow recipe for setting up MLflow and MinIO on a development machine.
About MLflow MLflow is an open-source platform designed to manage the complete machine learning lifecycle. Databricks created it as an internal project to address challenges faced in their own machine learning development and deployment processes. MLflow was later released as an open-source project in June 2018. As a tool for managing the complete lifecycle, MLflow contains the following components. - An engineer will use this feature the most. It allows experiments to be recorded and queried.
If you ever want to start over with a clean installation, then delete these folders. Next, this Docker Compose file is configuration driven. For example, instead of hard coding the PostgreSQL database name to `mlflow`, the name comes from the `config.env` file shown below using the following syntax in the Docker Compose file - `${PG_DATABASE}`.
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