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MLFlow UbiOps

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 deployment is configured as follows:

Deployment configuration
Name mlflow-deployment
Function A deployment that uses a trained AI model to predict the quality of wine
Input field: name: data, datatype: file
Output field: name: prediction, datatype: file
Version name v1
Description MLFlow deployment
Language python 3.6

How does it work?

Step 1: Login to your UbiOps account at and create an API token with project editor rights. To do so, click on Permissions in the navigation panel, and then click on API tokens. Click on [+]Add 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 role to the token: project editor. This role 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. You can use this file if you want to make a request to your newly build deployment.