> ## Documentation Index > Fetch the complete documentation index at: https://docs.getcollate.io/llms.txt > Use this file to discover all available pages before exploring further. # MLflow Connector > Connect MLflow to Collate seamlessly with our comprehensive connector guide. Learn setup, configuration, and ML model metadata integration in minutes. export const ConnectorDetailsHeader = ({name, icon, stage, availableFeatures, unavailableFeatures = [], availableFeaturesCollate = []}) => { const showSubHeading = availableFeatures?.length > 0 || unavailableFeatures?.length > 0 || availableFeaturesCollate?.length > 0; const totalAvailableFeatures = [...availableFeatures || [], ...availableFeaturesCollate || []]; return
{icon &&
{name}
}

{name}

{stage}
{showSubHeading &&
Feature List
{totalAvailableFeatures.map(feature =>
✓ {feature}
)} {unavailableFeatures.map(feature =>
✕ {feature}
)}
}
; }; This section provides guides and references to use the MLflow connector. Configure and schedule MLflow metadata and profiler workflows from the Collate UI: * [Requirements](#requirements) * [Metadata Ingestion](#metadata-ingestion) * [Troubleshooting](/ai-2-0/connectors/ml-model/mlflow/troubleshooting) ## Requirements To extract metadata, Collate needs two elements: * **Tracking URI**: Address of local or remote tracking server. More information on the MLflow documentation [here](https://www.mlflow.org/docs/latest/tracking.html#where-runs-are-recorded) * **Registry URI**: Address of local or remote model registry server. ## Metadata Ingestion To ingest metadata from MLflow, you need to create a service connection. The service connects MLflow with Collate. Once you create a service, Collate automatically starts ingesting metadata. ### Step 1: Add New Service 1. In the left navigation, click **Connections**. 2. On the **Connections** page, click **Add New Service**. Add New Service ### Step 2: Select a Service and Connector From the service type dropdown, select **Mlmodel Services**, then click the **MLflow** connector tile. Select Service ### Step 3: Add Service Name and Description * Enter a unique, descriptive **Service Name**. Collate identifies services by their service name. Enter a name that distinguishes this deployment from other MLflow services you are ingesting metadata from. * Optional: Enter a **Description** for the service. Add New Service Name **Note**: The service name cannot be changed after it is set. ### Step 4: Configure Connection Options Specify where ingestion runs, provide your source credentials, and verify the connection. #### Select Ingestion Runner Select an **Ingestion Runner**: the runner where the ingestion pipeline will execute. Add Name and Select Ingestion Runner #### Enter Connection Details Enter the connection details for MLflow. The right-hand panel in the UI displays inline help for each field. Configure Service Connection * **trackingUri**: Mlflow Experiment tracking URI. E.g., `http://localhost:5000` * **registryUri**: Mlflow Model registry backend. E.g., mysql+pymysql://mlflow:password\@localhost:3307/experiments #### Test Connection Once the credentials have been added, click on **Test Connection** and **Save** the changes. Test Connection ### Step 5: Configure Ingestion Options In the **What to Ingest** step, use filter patterns to control which assets Collate ingests from your ML model service. Filter patterns use regular expressions applied to model names. #### How Filter Patterns Work * **Include**: Add one or more comma-separated regular expressions. Collate ingests only assets whose names match at least one expression. Leave blank to include all assets. * **Exclude**: Add one or more comma-separated regular expressions. Collate skips any asset whose name matches an expression. Leave blank to exclude nothing. Rules match asset names using one of five expressions: * **contains**: matches any name containing the value. For example, `sales` matches `my_sales_data` and `sales_2024`. * **starts with**: matches names beginning with the value. For example, `prod_` matches `prod_db` and `prod_schema`. * **ends with**: matches names ending with the value. For example, `_raw` matches `events_raw` and `logs_raw`. * **is exactly**: matches the exact name only. For example, `analytics` matches only `analytics`. * **matches regex**: matches names using a regular expression. For example, `^prod_.*_v\d+$` matches `prod_events_v1`. When both Include and Exclude are set, Exclude takes priority. **Tip**: Leave the filter pattern empty to ingest all ML models available in the source. **Filter Options** The ML Model section includes the following filter options: * **ML Model**: Controls which ML models Collate ingests from the source. Each section provides the following controls: * **Scan Mode**: You can choose between the following scan modes: * **Scan all**: Ingests every asset of that type the connector can access. This is the default. * **Only specific**: Enables include rules so only assets matching at least one rule are ingested. * **Exclude system toggle**: Use this toggle to automatically filter out system-reserved names defined by the connector — for example, **Exclude system databases** for the Databases section. * **Always exclude**: Add permanent exclusion rules (shown in red). Assets matching these rules are never ingested, regardless of include rules. * **Preview**: Shows a real-time summary of what will be in scope based on your current rules. * **Include rules** *(available only in **Only specific** mode)*: Click **+ Add** to define a rule. Added rules appear as chips; an asset is included if it matches any rule. **Tip**: If [AutoPilot](/ai-2-0/admin-guide/applications/autopilot) is enabled, usage tracking, data lineage, and other downstream workflows start automatically after the first metadata ingestion completes. ### Step 6: Create & Deploy Click **Create & Deploy** to deploy the agent and start the first metadata ingestion run. Collate saves the service configuration and immediately begins pulling metadata from the source. To monitor ingestion progress or view the service you just added, go to **Connections** in the left navigation and select your service. ## Configure Metadata Agent and Schedule Ingestion The **Metadata Agent** extracts ML models, features, and other structural metadata from your source and keeps your Collate catalog in sync. It powers discovery, lineage, and governance across your data assets. When you click **Create & Deploy**, Collate automatically deploys a Metadata Agent for this service and triggers the first ingestion run. View its status and run history from the **Agents** tab on the service detail page. To configure the additional Metadata Agent and schedule ingestion, follow these steps: 1. In the left navigation, click **Connections** and select your service. 2. Click the **Agents** tab. 3. Click **Add Agent** and select **Metadata** from the dropdown. Add Metadata Agent For some services, the dropdown is not available and clicking **Add Agent** takes you directly to the agent configuration page. 4. On the **Configure Ingestion** page, do the following and click **Next**. * **Name this Ingestion**: Enter a unique recognizable name for this ingestion pipeline. Name this Ingestion * **Agent Setup**: Configure the core parameters for this agent. The following fields are available: | Field | Default | Description | | ---------------------- | ------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | Db Service Prefixes | — | List of database service name prefixes used to resolve lineage between ML models and database assets. Accepted formats: `DBService`, `DBService.Database`, `DBService.Database.Schema`, `DBService.Database.Schema.Table`. | | Mark Deleted ML Models | On | Soft-delete ML models in Collate when they are removed from the source. Associated entities like lineage are also deleted. | Agent Setup * **Filter Patterns**: Apply include or exclude rules to scope which ML models this agent ingests. These follow the same filter options described in **Step 5**. Filter Patterns * **Scope & Behaviour**: Control what metadata to include and how to handle deletions. Toggle each option on or off based on your needs: | Toggle | Default | Description | | ----------------- | ------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | Enable Debug Log | Off | Sets the ingestion log level to DEBUG. Useful for troubleshooting. | | Override Metadata | Off | When on, source values overwrite existing descriptions, tags, owners, and display names in Collate. When off, Collate only updates fields that have no existing value. | | Override Lineage | Off | When on, existing lineage is replaced with newly extracted lineage on each run. | Scope & Behaviour 5. On the **Schedule Interval** page, set when the agent runs: * **Schedule**: Choose a preset interval (Hourly, Daily, Weekly, Monthly) or enter a custom cron expression. * **On-Demand**: No automatic schedule; trigger the agent manually when needed. Schedule Interval 6. Click **Add** to deploy the agent. ## Troubleshooting Learn more about how to troubleshoot common MLflow connector issues and resolve configuration or ingestion errors.