Model Serving

A serving system loads model artifacts, accepts requests, runs inference, and returns outputs with required latency and reliability. Production model serving also involves scaling, versioning, monitoring, hardware selection, access control, and rollout management. The technique fits into the broader machine-learning lifecycle that includes data preparation, training, evaluation, deployment, and monitoring.