umlaut 0.0.1

Umlaut is a library for training and querying ML 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 all of them in the same way. Umlaut models can be as extensive as deep learning models or as simple as a reusable code block.

• Simple model lifecycle management
• Easily maintain and access multiple versions of the same model
• Quickly share business logic in reusable modules
• 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 block of business logic into an Umlaut compatible model
• query_model: Queries a previously trained model and saves audit metadata
• track_dataset: 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

Developing models with Umlaut

Custom models can be saved from any repository. Ensure the code block is in a Python Class and follow the example below.

from umlaut.core import Umlaut

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

    def predict(self, model_input: dict) -> bool:
        return self.business_logic()

if __name__ == "__main__":
    from umlaut.core import Umlaut

    model = Umlaut(model_name="example model")
    model.track_model(ExampleModel())

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.