Inference using Tecton

In this section of the notebook, we'll load our fraud detection model from mlflow, layer in the transaction information we'll assume we're passed in (type of transaction and amount), and layer it with features about the user we retrieve from Tecton.

Ordinarily, this would be running behind a REST API so you can do this inference in real time.

Here's what we get back from Tecton

And here's the final vector we'll pass to the model, incorporating the additional features we were passed in (transaction type columns) and Tecton features about the user in the correct order

As we load the model from databricks, you need to have all of the libraries installed that you use in your model, and using the same versions as well. You can see which versions are captured in your model by inspecting the conda.yaml file in your MLFlow run in your model directory under "Artifacts"