This is the root directory for an example project for the MLflow Regression Recipe. Follow the instructions here to set up your environment first, then use this directory to create a linear regressor and evaluate its performance, all out of box!
In this example, we demonstrate how to use MLflow Recipes to predict the fare amount for a taxi ride in New York City, given the pickup and dropoff locations, trip duration and distance etc. The original data was published by the NYC gov.
In this notebook (the Databricks version), we show how to build and evaluate a very simple linear regressor step by step, following the best practices of machine learning engineering. By the end of this example, you will learn how to use MLflow Recipes to
- Ingest the raw source data.
- Splits the dataset into training/validation/test.
- Create a feature transformer and transform the dataset.
- Train a linear model (regressor) to predict the taxi fare.
- Evaluate the trained model, and improve it by iterating through the
transformandtrainsteps. - Register the model for production inference.
All of these can be done with Jupyter notebook or on the Databricks environment. Finally, challenge yourself to build a better model. Try the following:
- Find a better data source with more training data and more raw feature columns.
- Clean the dataset to make it less noisy.
- Find better feature transformations.
- Fine tune the hyperparameters of the model.