umlaut 0.0.2

Umlaut simplifies deploying data science models

uMLaut

The uMLaut library simplifies model deployment and querying. It provides a single access point for all of your organizations models and an interface to interact with them. Umlaut models can be as extensive as deep learning models or as simple as a reusable code block.

uMLaut offers...

• simple model lifecycle management
• easy model maintenance
• access to multiple versions of the same model
• business logic sharing
• a user interface with MLflow
• audit tracking history (roadmap)
• auto-deployed models that can be queried through an API (roadmap)

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Umlaut Class

A Python class to assist with saving and querying business logic.

• track_model: Converts a data science model or block of business logic into an uMLaut compatible model
• query_model: Queries a previously trained model and saves audit metadata
• track_dataset (roadmap): Saves reporting datasets along with the initial query and underlying data that built it (roadmap)
• audit_model (roadmap): Retrieve the results of a model run for a historic date
• audit_dataset (roadmap): Retrieve a dataset as it was on a historic date

Deploying models with Umlaut

Custom data science models or business logic can be deployed simply by running umlaut.track_model(). Ensure the model code block is in a Python Class and follow the example below.

class ExampleModel():
    """
    Example business logic that can be wrapped into a model.
    The class _must_ contain a 'predict' method.
    """
    def business_logic(self, revenue: int) -> bool:
        if revenue > 5:
            return True
        else:
            return False

    def predict(self, model_input: dict) -> bool:
        return self.business_logic(revenue=model_input.get("revenue"))


if __name__ == "__main__":
    """Saves the model to MLflow in an experiment run"""
    from core import Umlaut

    umlaut = Umlaut()
    umlaut.track_model(
        model=ExampleModel(),
        model_name="Quarterly Revenue",
        run_name="Update",
    )

This will push the latest changes of ExampleModel() to MLflow as a new model version. Navigate to the MLflow Tracking Server to find the latest push and associate it to the MLflow model.

Querying models with Umlaut

Once a model is deployed in MLflow with umlaut.track_model(), it can be queried by calling umlaut.query_model().

from core import Umlaut

umlaut = Umlaut()
result = umlaut.query_model(
    model_name="Quarterly Revenue",
    input_config={"revenue": 3},
    stage="Staging",
)
print(f"Revenue will{'' if result else ' not'} exceed target")

If we query the simple Quarterly Revenue example model with revenue = 3, the model will return False as the revenue does not exceed the target of 5.