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MLflow Connector | OpenMetadata ML Model Integration | /connectors/ml-model/mlflow |
{% connectorDetailsHeader name="MLflow" stage="PROD" platform="OpenMetadata" availableFeatures=["ML Features", "Hyperparameters", "ML Store"] unavailableFeatures=[] / %}
In this section, we provide guides and references to use the MLflow connector.
Configure and schedule MLflow metadata and profiler workflows from the OpenMetadata UI:
{% partial file="/v1.7/connectors/ingestion-modes-tiles.md" variables={yamlPath: "/connectors/ml-model/mlflow/yaml"} /%}
Requirements
To extract metadata, OpenMetadata needs two elements:
- Tracking URI: Address of local or remote tracking server. More information on the MLflow documentation here
- Registry URI: Address of local or remote model registry server.
Metadata Ingestion
{% partial file="/v1.7/connectors/metadata-ingestion-ui.md" variables={ connector: "Mlflow", selectServicePath: "/images/v1.7/connectors/mlflow/select-service.png", addNewServicePath: "/images/v1.7/connectors/mlflow/add-new-service.png", serviceConnectionPath: "/images/v1.7/connectors/mlflow/service-connection.png", } /%}
{% stepsContainer %} {% extraContent parentTagName="stepsContainer" %}
Connection Details
- trackingUri: Mlflow Experiment tracking URI. E.g., http://localhost:5000
- registryUri: Mlflow Model registry backend. E.g., mysql+pymysql://mlflow:password@localhost:3307/experiments
{% /extraContent %}
{% partial file="/v1.7/connectors/test-connection.md" /%}
{% partial file="/v1.7/connectors/ml-model/configure-ingestion.md" /%}
{% partial file="/v1.7/connectors/ingestion-schedule-and-deploy.md" /%}
{% /stepsContainer %}