Skip to main content
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:

​
How to Run the Connector Externally

To run the Ingestion via the UI you’ll need to use the OpenMetadata Ingestion Container, which comes shipped with custom Airflow plugins to handle the workflow deployment. If, instead, you want to manage your workflows externally on your preferred orchestrator, you can check the following docs to run the Ingestion Framework anywhere.

External Schedulers

Get more information about running the Ingestion Framework Externally

​
Requirements

​
Python Requirements

We have support for Python versions 3.9-3.11
To run the MLflow ingestion, you will need to install: 
pip3 install "openmetadata-ingestion[mlflow]"

​
Metadata Ingestion

All connectors are defined as JSON Schemas. Here you can find the structure to create a connection to MLflow. In order to create and run a Metadata Ingestion workflow, we will follow the steps to create a YAML configuration able to connect to the source, process the Entities if needed, and reach the OpenMetadata server. The workflow is modeled around the following JSON Schema

​
1. Define the YAML Config

This is a sample config for MLflow:

​
2. Run with the CLI

First, we will need to save the YAML file. Afterward, and with all requirements installed, we can run: 
metadata ingest -c <path-to-yaml>
Note that from connector to connector, this recipe will always be the same. By updating the YAML configuration, you will be able to extract metadata from different sources.