MLflow Connector | Collate ML Model Integration

This section provides guides and references to use the MLflow connector. Configure and schedule MLflow metadata and profiler workflows from the Collate UI:

• Requirements
• Metadata Ingestion
• Troubleshooting

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Requirements

To extract metadata, Collate needs two elements:

• Tracking URI: Address of local or remote tracking server. For more information, see MLflow Tracking.
• Registry URI: Address of local or remote model registry server.

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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.

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Step 1: Add New Service

Open the Services page and start a new service.

1. Navigate to Settings > Services and select the ML Models service.
2. Click Add New Service.

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Step 2: Select a Service and Connector

From the service type dropdown, select Mlmodel Services, then click the MLflow connector tile.

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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.

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Step 4: Configure Connection Options

Specify where ingestion runs, provide your source credentials, and verify the connection.

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Select Ingestion Runner

Select an Ingestion Runner: the runner where the ingestion pipeline will execute.

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Enter Connection Details

Enter the connection details for MLflow. The right-hand panel in the UI displays inline help for each field.

• trackingUri: MLflow experiment tracking URI. For example, http://localhost:5000
• registryUri: MLflow model registry backend. For example, mysql+pymysql://mlflow:password@localhost:3307/experiments

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Test Connection

1. Click Test Connection to verify the credentials.
2. After the test succeeds, click Save.

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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 rules match model names using the following match types.

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How Filter Patterns Work

• Include: Add one or more comma-separated values. Each value uses one of the following match types. Collate ingests only assets whose names match at least one rule. Leave blank to include all assets.
• Exclude: Add one or more comma-separated values. Each value uses one of the following match types. Collate skips any asset whose name matches a rule. Leave blank to exclude nothing.

Rules match asset names using one of five match types:

• 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.

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Filter Options

The ML Model section includes the following filter options:

• ML Model: Controls which ML models Collate ingests from the source.

This section provides the following controls:

• Scan Mode: 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 filter out system-reserved names defined by the connector for that asset type.
• 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: In Only specific mode, click + Add to define a rule. Added rules appear as chips. An asset is included if it matches any rule.

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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 Settings > Services and select your service.

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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. Navigate to Settings > Services and select the ML Models service.
2. Select your service and click the Agents tab.
3. Click Add Agent and select Metadata from the dropdown. 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.
   • 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.

     
   • 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.
   • 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.

     
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.
   
6. Click Add to deploy the agent.

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Troubleshooting