ML platform

Every pricing model shared one aging pipeline: slow runs, a growing compute bill, and deploys that failed in serving and had to be crash-reverted in production.

A re-architected ML platform: a Snowflake-backed data layer feeding restructured Airflow DAGs, an optimized time-series cache for training and serving, and a pre-deployment testing framework that catches serving and environment failures before they reach production.

1. 01Snowflake data layer
2. 02Restructured Airflow DAGs
3. 03Optimized time-series cache
4. 04Pre-deployment test gate
5. 05Production serving

• DataSnowflake
• OrchestrationAirflow
• CachingCustom time-series cache
• TestingPre-deployment framework
• MonitoringDatadog

• Production pipeline runtime down 40%
• Compute costs down 43% — about $95K/year
• Pre-deployment gate ended the crash-and-revert cycle