---
title: Run the MLflow Connector Externally
slug: /connectors/ml-model/mlflow/yaml
---

{% 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:

- [Requirements](#requirements)
- [Metadata Ingestion](#metadata-ingestion)

{% partial file="/v1.6/connectors/external-ingestion-deployment.md" /%}

## Requirements

### Python Requirements

{% partial file="/v1.6/connectors/python-requirements.md" /%}

To run the MLflow ingestion, you will need to install:

```bash
pip3 install "openmetadata-ingestion[mlflow]"
```

## Metadata Ingestion

All connectors are defined as JSON Schemas.
[Here](https://github.com/open-metadata/OpenMetadata/blob/main/openmetadata-spec/src/main/resources/json/schema/entity/services/connections/mlmodel/mlflowConnection.json)
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](https://github.com/open-metadata/OpenMetadata/blob/main/openmetadata-spec/src/main/resources/json/schema/metadataIngestion/mlmodelServiceMetadataPipeline.json)

### 1. Define the YAML Config

This is a sample config for MLflow:

{% codePreview %}

{% codeInfoContainer %}

#### Source Configuration - Service Connection

{% codeInfo srNumber=1 %}

**trackingUri**: MLflow Experiment tracking URI. E.g., http://localhost:5000


{% /codeInfo %}

{% codeInfo srNumber=2 %}

**registryUri**: MLflow Model registry backend. E.g., mysql+pymysql://mlflow:password@localhost:3307/experiments

{% /codeInfo %}

{% partial file="/v1.6/connectors/yaml/ml-model/source-config-def.md" /%}

{% partial file="/v1.6/connectors/yaml/ingestion-sink-def.md" /%}

{% partial file="/v1.6/connectors/yaml/workflow-config-def.md" /%}

{% /codeInfoContainer %}

{% codeBlock fileName="filename.yaml" %}


```yaml {% isCodeBlock=true %}
source:
  type: mlflow
  serviceName: local_mlflow
  serviceConnection:
    config:
      type: Mlflow
```
```yaml {% srNumber=1 %}
      trackingUri: http://localhost:5000
```
```yaml {% srNumber=2 %}
      registryUri: mysql+pymysql://mlflow:password@localhost:3307/experiments
```

{% partial file="/v1.6/connectors/yaml/ml-model/source-config.md" /%}

{% partial file="/v1.6/connectors/yaml/ingestion-sink.md" /%}

{% partial file="/v1.6/connectors/yaml/workflow-config.md" /%}

{% /codeBlock %}

{% /codePreview %}

{% partial file="/v1.6/connectors/yaml/ingestion-cli.md" /%}