Learn what it takes to build account takeover fraud detection systems from a data engineering and production deployment perspective. The post Rethinking Feature Engineering for ATO Detection appeared first on Tecton.| Tecton
Build drift-aware ML systems with Tecton for feature engineering with consistent offline/online serving and Arize for tracking data quality, performance and drift.| Tecton
Learn how Tecton's new latency budgets let you control tradeoffs between speed and accuracy in real-time AI, enabling faster fraud detection and more resilient ML systems. The post Fraud Doesn’t Wait: Accelerating AI-Driven Detection with Latency Budgets appeared first on Tecton.| Tecton
How Coinbase productionizes user sequence features with Tecton and Databricks to power fraud and recommendation ML models. The post How Coinbase Builds Sequence Features for Machine Learning appeared first on Tecton.| Tecton
Build drift-aware ML systems with Tecton's feature engineering and offline/online serving plus Fiddler's feature and model drift analysis. The post Preventing Model Decay: Tecton + Fiddler for ML Drift Detection appeared first on Tecton.| Tecton
Learn how Tecton and Taktile enable fraud and risk teams to iterate faster, reduce fraud losses, and make more accurate decisions at scale. The post How Tecton and Taktile Power Real-Time Risk Decisions at Scale appeared first on Tecton.| Tecton
Learn why feature freshness stops fraud. Real-time data catches attacks instantly, while batch systems create windows for more fraudulent transactions.| Tecton
Learn how to solve the feature freshness problem in real-time ML systems. See how HomeToGo achieved sub-second data freshness without building complex infrastructure.| Tecton
How North built a real-time fraud detection system that processes millions of payments using machine learning. A technical overview of their ML architecture, challenges, and solutions.| Tecton