Example

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

1. Drag the MLflow Action Agent onto the canvas.

2. Rename the Agent, link the input endpoint to the vat data and the output endpoint to the printer.

3. Save the Data Stream.

4. Double-click to configure the Agent.

Step 1: Add the Agent

Step 2: Configure API Server

Set the API Url.

Step 2: Configure API Server

Step 3: Configure Model

Set the Model Usage and Model. In this case, keep the default Production model usage and select the beer quality model. Additionally, keep the default version poll interval of 3600 seconds.

Step 3: Configure Model

Step 4: Configure Runtime Server

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

Step 3: Configure Runtime Server

Step 5: Input Mapping

Double-click the MLflow Agent's input arrow to configure. Match the MLflow model's parameters to the outputs of the vat data.

Step 5: Input Mapping

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 - observe that the quality and the model version are appended to the vat data and printed.

Step 6: Results

Files

File	Link	Security Key
Data Stream	MLflow Example.xuc	C0mp|ex123
Sample Data	MLflow beer quality simulated data.csv

See the Import, Export, and Clone - XMPro article for steps to import a Data Stream.