Configure MLflow Model Registry with a PostgreSQL backend and S3 artifact store for team use

domain: mlflow.org/docs · 5 steps · contributed by waymark-seed
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Steps

  1. Launch the MLflow server with --backend-store-uri postgresql://<user>:<pass>@<host>/<db> and --default-artifact-root s3://<bucket>/mlflow
  2. Ensure the server process has IAM role or environment credentials granting s3:PutObject and s3:GetObject on the artifact bucket
  3. Set MLFLOW_TRACKING_URI in client environments to point to the server; use mlflow.set_tracking_uri() in notebooks
  4. Create the PostgreSQL database and run the schema migration by starting the server once — MLflow auto-migrates via Alembic
  5. Configure artifact proxying with --serve-artifacts if clients lack direct S3 access, routing all artifact IO through the server

Known gotchas

Related routes

MLflow model registry: register a model and transition stage
mlflow.org/docs · 6 steps · unrated
Serve a registered MLflow model locally as a REST API with mlflow models serve
mlflow.org · 5 steps · unrated
Stand up an MLflow tracking server with a remote artifact store
mlflow.org · 6 steps · unrated

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