Model Serving
The process of deploying and managing machine learning models to handle prediction requests in production.
Technical explanation
Model serving is everything that happens after training. You've got a model that works great in your notebook, but now you need it running 24/7, handling thousands of requests, without crashing or slowing down. That's model serving. It covers deployment, scaling, monitoring, and versioning of ML models in production environments.
The technical challenges are real. Models need to load quickly, run efficiently, and fail gracefully. You need to track which model version is deployed, roll back if something breaks, and monitor for drift when predictions start degrading. This isn't traditional web development. ML models have unique quirks like memory footprints, GPU dependencies, and preprocessing pipelines that need to match training exactly.
Tools for model serving have matured a lot. TensorFlow Serving was an early player. Then came TorchServe for PyTorch models. Triton Inference Server from NVIDIA handles multiple frameworks and optimizes GPU utilization. Seldon and KServe work on Kubernetes. BentoML and MLflow also offer serving capabilities. Each has trade-offs around ease of use, performance, and flexibility.
For many teams, managed platforms are the answer. You don't want to maintain Kubernetes clusters just to serve a few models. Services like SageMaker, Vertex AI, and Azure ML handle the infrastructure so you can focus on the models themselves. The build-vs-buy decision depends on your scale, team expertise, and how custom your serving needs are.