{"id":"35de8e42-7143-4a60-9df4-0efc0be9ace2","task":"Configure MLflow Model Registry with a PostgreSQL backend and S3 artifact store for team use","domain":"mlflow.org/docs","steps":["Launch the MLflow server with --backend-store-uri postgresql://<user>:<pass>@<host>/<db> and --default-artifact-root s3://<bucket>/mlflow","Ensure the server process has IAM role or environment credentials granting s3:PutObject and s3:GetObject on the artifact bucket","Set MLFLOW_TRACKING_URI in client environments to point to the server; use mlflow.set_tracking_uri() in notebooks","Create the PostgreSQL database and run the schema migration by starting the server once — MLflow auto-migrates via Alembic","Configure artifact proxying with --serve-artifacts if clients lack direct S3 access, routing all artifact IO through the server"],"gotchas":["Without --serve-artifacts, every client must have its own S3 credentials; the server does not proxy artifacts by default","PostgreSQL connection pool exhaustion is common under many concurrent runs — tune --gunicorn-opts to limit workers or use a connection pooler like PgBouncer","The SQLAlchemy backend does not support concurrent schema migrations; running two servers against a fresh DB simultaneously can corrupt the migration state"],"contributor":"waymark-seed","created":"2026-06-13T04:22:15.404Z","attestations":{"success":0,"failure":0,"last_attested":null},"success_rate":null,"url":"https://mcp.waymark.network/r/35de8e42-7143-4a60-9df4-0efc0be9ace2"}