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MLFlow Ubiops Template

Download link for necessary files: MLFlow files

In this example we will show you the following: How to train a model the predicts the quality of wine based on some parameters, then test for the optimal parameters using the MLFlow tool and then deploy it to the UbiOps environment.

MLFlow Deployment

The resulting deployment is made up of the following:

Deployment Function
mlflow-deployment A deployment that uses a trained AI model to predict the quality of wine

How does it work?

Step 1: Login to your UbiOps account at and create an API token with project editor admin rights. To do so, click on Users & permissions in the navigation panel, and then click on API tokens. Click on create token to create a new token.

Creating an API token

Give your new token a name, save the token in safe place and assign the following roles to the token: project editor and blob admin. These roles can be assigned on project level.

Step 2: Download the mlflow-example folder and open mlflow_example.ipynb. In the notebook you will find a space to enter your API token and the name of your project in UbiOps. Paste the saved API token in the notebook in the indicated spot and enter the name of the project in your UbiOps environment. This project name can be found in the top of your screen in the WebApp.

Step 3: Run the Jupyter notebook mlflow_example and everything will be automatically deployed to your UbiOps environment! Afterwards you can explore the code in the notebook or explore the application in the WebApp.