Interpretability and Reproducability in Production Machine Learning Applications

Abstract

Explainability/Interpretability in machine learning applications is becoming critical, with legal and industry requirements demanding human understandable machine learning results. We describe the additional complexities that occur when a known interpretability technique (canary models) is applied to a real production scenario. We furthermore argue that reproducibility is a key feature in practical usages of such interpretability techniques in production scenarios. With this motivation, we present a production ML reproducibility solution, namely a comprehensive time ordered event sequence for machine learning applications. We demonstrate how our approach can bring this known common interpretability technique into production viability. We further present the system design and early performance characteristics of our reproducibility solution.