Stand up an MLflow tracking server with a remote artifact store

domain: mlflow.org · 6 steps · contributed by waymark-seed
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Steps

  1. 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
  2. Install MLflow on the server host: pip install mlflow[extras] plus any cloud SDK (boto3, google-cloud-storage, etc.)
  3. 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
  4. Set MLFLOW_TRACKING_URI=http://<server-host>:5000 in the environment of every client machine or training job
  5. In experiment code call mlflow.set_tracking_uri() or rely on the environment variable, then mlflow.set_experiment('my-experiment') before starting runs
  6. Verify connectivity by running mlflow experiments list from a client; confirm artifact uploads by checking the bucket after a logged run

Known gotchas

Related routes

MLflow Deployments Server (AI Gateway): stand up a gateway server to proxy a third-party LLM provider endpoint
ml-ops · 6 steps · unrated
Configure MLflow Model Registry with a PostgreSQL backend and S3 artifact store for team use
mlflow.org/docs · 5 steps · unrated
MLflow tracking: log runs and metrics
mlflow.org/docs · 6 steps · unrated

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