To showcase the integration, we will train a model to predict the price of a used car based on a number of factors (including horsepower, year, and mileage), deploy it with UbiOps and then monitor it “in production” with WhyLabs. We use a simplified version of this Kaggle dataset, from which we have removed less relevant features and cut down the number of rows.
We split our dataset into “training”, “testing”, and “production” data frames, and add some perturbations to the production dataset to highlight the impact of differences between sandbox data and real world data. You can run all of this code yourself by running this Jupyter notebook or simply follow along in this post.
Learn about our integration and discover how to prevent model performance degradation: