Calibration

AI debugging and model monitoring AI debugging and model monitoring mix software quality work with data-driven observability. Models in production face data shifts, new user behavior, and labeling quirks that aren’t visible in development. The goal is to detect problems early, explain surprises, and keep predictions reliable, fair, and safe for real users. What to monitor helps teams act fast. Track both system health and model behavior. Latency and reliability: response time, error rate, timeouts. Throughput and uptime: how much work the system handles over time. Prediction errors: discrepancies with outcomes when labels exist. Data quality: input schema changes, missing values, corrupted features. Data drift: shifts in input distributions compared with training data. Output drift and calibration: changes in predicted probabilities versus reality. Feature drift: shifts in feature importance or value ranges. Resource usage: CPU, memory, GPU, and memory leaks. Incidents and alerts: correlate model issues with platform events. How to instrument effectively is essential. Start with a simple observability stack. ...

Practical Computer Vision for Industry Industrial environments demand vision systems that are reliable, repeatable, and easy to maintain. Practical computer vision focuses on decisions you can test on the shop floor: steady lighting, simple models, and clear pass/fail criteria. A good system reduces manual checks, speeds up lines, and keeps data ready for audits. Even with limited data, you can build solid inspection by pairing good sensing with straightforward rules. Document decisions so engineers can audit results and plan maintenance, calibration checks, and occasional retraining. ...