Model Performance Monitoring

Production inference and ground truth data are analyzed to detect model drift and performance degradation.

Business impact

• Model accuracy — Maintains prediction correctness by identifying performance degradation promptly
• Model stability — Monitors consistency of model outputs to prevent unexpected behavior over time
• Model drift — Detects shifts in data or concept distributions that impact model validity

Data requirements

• Production inference data (Structured) — Used to evaluate real-time model predictions against expected outcomes
• Ground truth labels (Structured) — Provides actual outcomes to compare with model predictions for accuracy checks
• Model training and validation datasets (Structured) — Serves as reference data to detect drift and data quality issues

AI methods and techniques

• Predictive AI — Analyzes performance metrics and detects anomalies indicating model degradation
• Symbolic AI — Applies rule-based checks and statistical tests for drift and data quality monitoring

AI models and model families

GPT-4o, Llama, Claude

Industries

Real-world evidence

1 documented case study on record.

Companies using this: Tempus.