A simpler way to work with MLflow. Umlaut simplifies model deployment and operational analytics for data teams. Centralize critical business logic and track all uses in a single location. Umlaut is built on top of MLflow and offers a simple Python class to assist with tracking and running models. Umlaut is designed to be used by data teams of all sizes and is a great tool for small teams without dedicated data scientists.
Umlaut provides a simple Python class to assist with saving and running models in MLflow. The class has two methods:
track_model: Converts a data science model or block of business logic into an MLflow compatiblemodelrun_model: Runs a previously trainedmodeland saves audit metadata
- simple commands to track and run models
- history of all inputs and results for model runs
- model lifecycle management
- access to multiple versions of the same model
- a user interface with
MLflow - model audit history tracking (roadmap)
- auto-deployed models that can be queried through an API (roadmap)
pip install umlaut
MLflow is a powerful machine learning library created by Databricks for data science teams. It offers an extensive API for tracking and running models, but the learning curve can be a deterrent for small teams without dedicated data scientists. Umlaut strips away much of the complexity of MLflow while maintaining the immense value of tracking and running your models in a single location.
MLflow has two requirements:
- A model artifact storage location
- This can be a local directory or a cloud storage URI. More info in the MLflow docs.
- A model registry
- The model registry is where model changes and run data are stored. More info in the MLflow docs.
An mlflow server must be running in order to work with Umlaut. The command to start an MLflow server with local artifact storage and a Postgres model registry is as follows:
mlflow server --backend-store-uri postgresql+psycopg2://admin:password@localhost:5432/database --default-artifact-root "mlruns/"
Once the server is running you can navigate to the MLflow UI and begin interacting with models.
Umlaut offers a simple Python class to assist with saving and running business logic in MLflow. The class has two methods:
track_model: Converts a data science model or block of business logic into an MLflow compatiblemodelrun_model: Runs a previously trainedmodeland saves audit metadata
Custom models can be deployed simply by running track_model(). Ensure that 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 'run' method with the input config
mapped to the corresponding model parameters."""
def business_logic(self, revenue: int) -> bool:
return revenue > 5
def run(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 umlaut import Umlaut
Umlaut().track_model(
model=ExampleModel(),
model_name="Revenue Forecast",
)
This will push the latest changes of ExampleModel() to MLflow as a new model version. Navigate to the MLflow server where you can find details for the example "Quarterly Revenue" model.
Once a model is deployed in MLflow with track_model(), it can be run by calling run_model().
from umlaut import Umlaut
result = Umlaut().run_model(
model_name="Revenue Forecast",
input_config={"revenue": 3},
stage="Staging",
)
print(f"Revenue will{'' if result else ' not'} exceed target")
Running the simple Revenue Forecast model with revenue = 3 will return False as the revenue does not exceed the target of 5. The call to the model will be tracked in MLflow with model inputs and results.
MLflow provides a useful interface for interacting with models and visualizing their performance.
