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This example demonstrates how to use this AI & ML Agent to predict beer quality for vat data.
Refer to configuration to understand all configuration options of this Agent.

Step 1: Add the Agent

Drag the MLflow Agent onto the canvas, link the input endpoint to the vat data, and the output to the printer. Rename the Agent and save the Data Stream.

Step 2: Configure General

Select the Agent and click Configure. Keep the default Collection.

Step 3: Configure API Server

Set the API Url and Model. In this case, keep the default version poll interval of 3600 seconds to check hourly if a new version of the model has been published.

Step 4: Configure Runtime Server

Set the Runtime Url.
Apply the changes and save the Data Stream.

Step 5: Input Mapping

Select the MLflow Agent's input arrow and click Configure. Match the properties of the MLflow Agent with the outputs of the vat data.

Step 6: Results

Apply the changes, save the Data Stream, and publish it.
Let's look at the Live Data View. The MLflow model returns a prediction based on the input vat data - the quality and the model version are appended to the vat data and printed.


Security Key
Data Stream
MLflow Example.xuc
Sample Data
MLflow beer quality simulated data.csv
See the Import, Export, and Clone - XMPro article for steps to import a Data Stream.