The Model’s Shipped; What Could Possibly go Wrong?

Model observability plays a crucial role in detecting, diagnosing, and explaining regressions in deployed machine learning models. Some potential failure modes include Concept Drift, where the underlying task of a model changes over time; Data Drift or Feature Drift, which occurs when the distribution of model inputs changes; Training-prod skew, where the distribution of training data differs from production data; and Cascading Model Failures, which happen when multiple models are interconnected. Additionally, Outliers can be problematic as they may represent edge cases or adversarial attacks on a model. Monitoring tools help identify these issues and enable teams to improve their models after deployment.

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