What is Model Serving? - Definition & Meaning
Learn what model serving is, how AI models are exposed in production, and which tools and best practices exist for scalable AI deployment.
Definition
Model serving is the process of making a trained AI model available as a service that delivers predictions (inference) via APIs or endpoints. It includes hosting, load balancing, scaling, and monitoring.
Technical explanation
Model serving involves loading model artifacts, handling requests, pre- and postprocessing, and returning responses. Popular frameworks: TensorFlow Serving, TorchServe, Triton Inference Server, and vLLM for LLMs. Cloud deployment often uses managed services (SageMaker, Vertex AI, Azure ML). Key aspects: versioning (A/B testing, rollbacks), scaling (horizontal/vertical), batching for efficiency, and monitoring (latency, throughput, errors). Edge serving runs models locally on devices.
How AVARC Solutions applies this
AVARC Solutions brings AI models to production via model serving. We use containerized deployment (Docker, Kubernetes) for scalability, implement health checks and monitoring, and choose the right serving infrastructure (cloud vs. on-premise) based on client requirements.
Practical examples
- An e-commerce company serving a recommendation model via a REST API, with automatic scaling during peak load.
- A support tool serving an intent classification model with low latency for real-time ticket routing.
- A document analysis service serving a custom NLP model in a Kubernetes cluster with canary deployments.
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