Production ML

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. ...

Machine Learning in Production: MLOps Essentials In production, machine learning models live in a real world of data shifts, traffic spikes, and changing business needs. MLOps is the set of practices that keep models reliable, updated, and safe. It blends data science with software engineering, operations, and governance. A typical ML project moves through stages: data collection, feature engineering, model training, evaluation, deployment, monitoring, and updates. The goal of MLOps is to make each stage repeatable, auditable, and resilient to change. ...