Designed and implemented a comprehensive quality architecture for a machine learning recommendation engine processing 5M+ daily transactions. The solution addressed unique challenges in testing AI systems including model drift, data quality validation, and performance under load.
Challenge
The client’s recommendation engine was producing inconsistent results in production, but the team lacked systematic ways to validate model behavior or catch regressions before deployment. Testing was ad-hoc and reactive.
Solution
Developed a testing pyramid specifically for ML systems, with unit tests for feature engineering, integration tests for data pipelines, and contract tests for API endpoints
Implemented automated validation of embedding quality and semantic consistency
Created monitoring system for model drift and data skew
Established A/B testing framework for recommendation quality