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MLflow

MLflow

PROD
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:

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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.

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Metadata Ingestion

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Visit the Services Page

Click `Settings` in the side navigation bar and then `Services`. The first step is to ingest the metadata from your sources. To do that, you first need to create a Service connection first. This Service will be the bridge between OpenMetadata and your source system. Once a Service is created, it can be used to configure your ingestion workflows.Visit Services Page
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Create a New Service

Click on _Add New Service_ to start the Service creation.Create a new Service
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Select the Service Type

Select MLflow as the Service type and click _Next_.Select Service
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Name and Describe your Service

Provide a name and description for your Service.

Service Name

OpenMetadata uniquely identifies Services by their **Service Name**. Provide a name that distinguishes your deployment from other Services, including the other MLflow Services that you might be ingesting metadata from. Note that when the name is set, it cannot be changed.Add New Service
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Configure the Service Connection

In this step, we will configure the connection settings required for MLflow. Please follow the instructions below to properly configure the Service to read from your sources. You will also find helper documentation on the right-hand side panel in the UI.Configure Service connection

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Connection Details

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Connection Details

When using a Hybrid Ingestion Runner, any sensitive credential fields—such as passwords, API keys, or private keys—must reference secrets using the following format:
password: secret:/my/database/password
This applies only to fields marked as secrets in the connection form (these typically mask input and show a visibility toggle icon). For a complete guide on managing secrets in hybrid setups, see the Hybrid Ingestion Runner Secret Management Guide.
  • trackingUri: Mlflow Experiment tracking URI. E.g., http://localhost:5000
  • registryUri: Mlflow Model registry backend. E.g., mysql+pymysql://mlflow:password@localhost:3307/experiments
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Test the Connection

Once the credentials have been added, click on Test Connection and Save the changes.Test Connection
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7. Configure Metadata Ingestion

In this step we will configure the metadata ingestion pipeline, Please follow the instructions belowConfigure Metadata Ingestion
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Metadata Ingestion Options
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  • Name: This field refers to the name of ingestion pipeline, you can customize the name or use the generated name.
  • Mark Deleted Ml Models (toggle):: Set the Mark Deleted Ml Models toggle to flag ml models as soft-deleted if they are not present anymore in the source system.
  • ML Model Filter Pattern (Optional): To control whether to include an ML Model as part of metadata ingestion.
    • Include: Explicitly include ML Models by adding a list of comma-separated regular expressions to the Include field. OpenMetadata will include all ML Models with names matching one or more of the supplied regular expressions. All other ML Models will be excluded.
    • Exclude: Explicitly exclude ML Models by adding a list of comma-separated regular expressions to the Exclude field. OpenMetadata will exclude all ML Models with names matching one or more of the supplied regular expressions. All other ML Models will be included.
  • Enable Debug Log (toggle): Set the Enable Debug Log toggle to set the default log level to debug.
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    Schedule the Ingestion and Deploy

    Scheduling can be set up at an hourly, daily, weekly, or manual cadence. The timezone is in UTC. Select a Start Date to schedule for ingestion. It is optional to add an End Date.Review your configuration settings. If they match what you intended, click Deploy to create the service and schedule metadata ingestion.If something doesn’t look right, click the Back button to return to the appropriate step and change the settings as needed.After configuring the workflow, you can click on Deploy to create the pipeline.Schedule the Workflow
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    View the Ingestion Pipeline

    Once the workflow has been successfully deployed, you can view the Ingestion Pipeline running from the Service Page.View Ingestion Pipeline
    If AutoPilot is enabled, workflows like usage tracking, data lineage, and similar tasks will be handled automatically. Users don’t need to set up or manage them - AutoPilot takes care of everything in the system.