Exposing a MLflow model

Note

See Exposing a Visual Model to learn about exposing a visual model. Exposing a MLflow model relies on the same basis as a virtual model.

Deploying the model

A MLflow model can be deployed using the API, as described in Importing MLflow models . It can also be deployed from an Experiment Tracking run. See Deploying MLflow models for more information.

Exposing the model

Once deployed, a MLflow model can be exposed nearly like a visual model. Even so, the MLflow Model output is to be set in the endpoint settings. It can be either raw data or restructured . The first outputs directly what the MLflow model outputs while the second makes DSS try to restructure it (disable this in case of compatibility issues).

For example, a SKLearn binary classification typically outputs a prediction probability. Restructure enriches it to a prediction and probabilities for both label.

See Using MLflow models in DSS .