Flyte
2 use cases using this technology
How Schibsted's AI model helped boost subscription sales
Schibsted
Schibsted developed a machine learning model to serve personalized subscription-sales recommendations to anonymous, non-logged-in front-page readers, using first-party demographic data (age/gender predictions from advertising) and sales insights to generate real-time, on-demand recommendations rather than batch processing. Of 158 candidate data points tested, around a dozen were retained as model features. A/B tests showed a 75 percent increase in subscription sales from front-page articles compared to previous models. The system is integrated with Schibsted's Curate content recommendation platform, using Flyte for orchestration and AWS DJL for model inferencing, and the team is transitioning to Tecton as a managed feature store.
Stripe's ML flywheel cuts successful card-testing fraud attacks by 80%
Stripe
Stripe built a machine learning-based system to detect and block card testing fraud, applying ML models at three levels of abstraction: overall prevalence estimation, identifying where attacks are occurring, and scoring individual transactions. A rapid data-labeling, retraining and redeployment pipeline, built on Stripe's Shepherd feature-engineering platform (developed with Airbnb) and the Flyte ML orchestration platform, lets the team react to new attack patterns within hours. The system is augmented by a large transformer model trained on billions of global transactions that generates embeddings used across card-testing detection use cases. Successful card-testing attacks on Stripe declined by 80% over two years even as Stripe's payment volume grew past $1 trillion.