AI Observability is the practice of collecting and analyzing detailed signals from AI systems to understand what is happening inside them and why their behavior changes. It goes beyond checking whether a model is running by examining inputs, outputs, model performance, latency, resource consumption, errors, and data quality across the entire AI pipeline.
AI observability helps teams investigate unexpected predictions, identify data or model drift, trace failures, and understand relationships between model behavior and underlying infrastructure. It is particularly valuable for complex production environments where multiple models, services, datasets, and deployment components interact.
