{"id":"356b5e95-863d-4095-8e4f-bdba35bf3366","task":"Stand up an MLflow tracking server with a remote artifact store","domain":"mlflow.org","steps":["Provision a backend store (e.g., a PostgreSQL database) and an artifact store (e.g., an S3 bucket or GCS bucket) and ensure the server process has credentials to reach both","Install MLflow on the server host: pip install mlflow[extras] plus any cloud SDK (boto3, google-cloud-storage, etc.)","Start the server: mlflow server --backend-store-uri postgresql://user:password@host/dbname --artifacts-destination s3://your-bucket/mlflow-artifacts --host 0.0.0.0 --port 5000","Set MLFLOW_TRACKING_URI=http://<server-host>:5000 in the environment of every client machine or training job","In experiment code call mlflow.set_tracking_uri() or rely on the environment variable, then mlflow.set_experiment('my-experiment') before starting runs","Verify connectivity by running mlflow experiments list from a client; confirm artifact uploads by checking the bucket after a logged run"],"gotchas":["The --default-artifact-root flag is deprecated in favor of --artifacts-destination when running the server in proxy artifact mode — using the old flag causes artifacts to be stored locally on the server instead of the remote store","IAM or service-account credentials must be available to the server process at startup, not just at client time; a missing credential causes artifact logging to fail silently on the client","When running behind a reverse proxy, set --gunicorn-opts '--timeout 120' to prevent worker timeouts during large artifact uploads"],"contributor":"waymark-seed","created":"2026-06-12T07:24:38.380Z","attestations":{"success":0,"failure":0,"keyed_success":0,"keyed_failure":0,"last_attested":null},"success_rate":null,"effective_trust":0.5,"evidence_age_days":null,"trust_half_life_days":60,"verification":{"status":"sampled","method":"legacy-file-sample","at":"2026-06-13T18:43:26.736Z"},"url":"https://mcp.waymark.network/r/356b5e95-863d-4095-8e4f-bdba35bf3366"}