Inference

Model Serving

Model serving is the operational layer enabling production inference. It includes: load balancing, autoscaling, monitoring, error handling, and versioning. Serving infrastructure must be reliable, fast, and scalable. Common approaches: containers (Docker), serverless functions, managed services. Serving adds non-trivial overhead. Understanding serving requirements (latency SLAs, throughput) drives architecture. Production serving is more complex than training-time inference.