This is the root directory for an example project for the MLflow Classification Recipe. Follow the instructions here to set up your environment first, then use this directory to create a classifier and evaluate its performance, all out of box!
In this example, we demonstrate how to use MLflow Recipes to build a model to predict whether a bottle of wine is red or white based on the wine's physicochemical properties. The example uses a dataset from the UCI Machine Learning Repository, presented in Modeling wine preferences by data mining from physicochemical properties Cortez et al., 2009.
In this notebook (the Databricks version), we show how to build and evaluate a very simple classifier 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 an identity transformer and transform the dataset.
- Train a linear model (classifier) to tell if a bottle of wine is red.
- 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.