We have been working with Paddy Power, an Irish gambling company, to help them calculate odds for horse racing by using two different models: one for All Weather races and one for Flat races. Paddy Power also uses Hopsworks with batch predictions as an anti-cheating system making sure that no user “knows more than the betting company”.
Improved models that generate more revenue
Access to statistics and metadata, decreasing the time to generate training datasets
Discover pre-computed features, types of those features, descriptive statistics and the distribution of feature values
Previously engineered and quality-assured features become available to be reused - ready for training
Feature engineering code is not duplicated in applications, instead a single pipeline computes features for serving and training
Data scientists can concentrate on improving models, and not on complex infrastructure for ensuring training and serving pipelines are kept in sync
Replacing SQL-based feature pipelines with Python, and improving speed of feature engineering: Paddy Power determines betting prices with the help of predictions generated from ML models.