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What is it?

MLflow is an open source platform to manage the ML lifecycle, including experimentation, reproducibility, deployment, and a central model registry. MLflow currently offers four components: The 4 capabilities of MLFlow. Source: Read more here:

Helm installation into OpenShift namespace


  • Install the "Crunchy Postgres for Kubernetes" operator (can be found in OperatorHub) - To store the MLFlow config
  • Install the "OpenShift Data Foundation" operator (can be found in OperatorHub) - To provide S3 storage for the experiments and models


<Create an OpenShift project, either through the OpenShift UI or 'oc new-project project-name'>
helm repo add strangiato
helm repo update
<Log in to the correct OpenShift project through 'oc project project-name'>
helm upgrade -i mlflow-server strangiato/mlflow-server

Additional Options

The MLFlow Server helm chart provides a number of customizable options when deploying MLFlow. These options can be configured using the --set flag with helm install or helm upgrade to set options directly on the command line or through a values.yaml file using the --values flag.

For a full list of configurable options, see the helm chart documentation:

OpenDataHub Dashboard Application Tile

As discussed in the Dashboard Configuration, ODH/RHOAI allows administrators to add a custom application tile for additional components on the cluster.

Enabled tile

The MLFlow Server helm chart supports creation of the Dashboard Application tile as a configurable value. If MLFlow Server is installed in the same namespace as ODH/RHOAI you can install the dashboard tile run the following command:

helm upgrade -i mlflow-server strangiato/mlflow-server \
    --set odhApplication.enabled=true

The MLFlow Server helm chart also supports installing the odhApplication object in a different namespace, if MLFlow Server is not installed in the same namespace as ODH/RHOAI:

helm upgrade -i mlflow-server strangiato/mlflow-server \
    --set odhApplication.enabled=true \
    --set odhApplication.namespaceOverride=redhat-ods-applications

After enabling the odhApplication component, wait 1-2 minutes and the tile should appear in the Explorer view of the dashboard.


This feature requires ODH v1.4.1 or newer

Test MLFlow

  • Go to the OpenShift Console and switch to Developer view.
  • Go to the Topology view and make sure that you are on the MLFlow project.
  • Check that the MLFlow circle is dark blue (this means it has finished deploying).
  • Press the "External URL" link in the top right corner of the MLFlow circle to open up the MLFlow UI.
  • Run helm test mlflow-server in your command prompt to test MLFlow. If successful, you should see a new experiment called "helm-test" show up in the MLFlow UI with 3 experiments inside it.

Adding MLFlow to Training Code

import mlflow
from sklearn.linear_model import LogisticRegression

# Set tracking URI

# Setting the experiment

if __name__ == "__main__":
    # Enabling automatic logging for scikit-learn runs

    # Starting a logging run
    with mlflow.start_run():
        # train

Source Code

MLFlow Server Source Code:

MLFlow Server Helm Chart Source Code:


  • Credit Card Fraud Detection pipeline using MLFlow together with RHOAI: Demo