Machine Learning Model Serving

Machine learning model serving is the infrastructure and practice of deploying trained models so they can receive input data and return predictions in real time or in batch. It bridges the gap between a model that works in a notebook and one that runs reliably in production at scale.

itArtificial intelligence and machine learning

Machine Learning Model Serving

Training a model is half the job. The other half is making that model available to applications that need its predictions. Model serving is the infrastructure layer that takes a trained model artifact and exposes it as a service — accepting input, running inference, and returning results with the reliability, latency, and throughput that production systems demand.

Why model serving is a distinct discipline

A model in a Jupyter notebook runs on one machine, processes one request at a time, and has no availability requirement. A model in production must handle concurrent requests, meet latency SLAs, survive hardware failures, scale under load, manage multiple model versions, and integrate with the rest of your software architecture.

These requirements push model serving beyond "export and call the predict function." You need standardized APIs, request batching, hardware-aware optimization, load balancing, versioning, canary deployments, and observability — the same concerns that exist for any production service, plus ML-specific concerns like model warmup, dynamic batching, and framework compatibility.

Machine Learning Model Serving

Why model serving is a distinct discipline

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