Model Serving

Model serving is the practice of deploying trained machine learning models as accessible services that receive inference requests, process inputs through the model, and return predictions or generated outputs. It is the critical bridge between model development and production value, encompassing the infrastructure, APIs, scaling mechanisms, and operational tooling needed to serve models reliably at scale.

Model serving infrastructure must address several technical challenges: managing request routing and load balancing across model replicas, dynamic batching to maximize GPU utilization by grouping multiple requests into a single forward pass, auto-scaling to handle variable request volumes, model versioning and canary deployments for safe updates, health monitoring and automatic recovery, and multi-model serving on shared infrastructure to optimize resource utilization.

Popular model serving frameworks include NVIDIA Triton Inference Server, which supports multiple model formats and frameworks with advanced batching and ensemble capabilities; vLLM, which provides high-throughput LLM serving with paged attention; TensorFlow Serving for TensorFlow models; TorchServe for PyTorch models; KServe (formerly KFServing) for Kubernetes-native model serving; and Seldon Core for complex inference graphs. Each framework offers different trade-offs in terms of supported model types, optimization capabilities, and operational complexity.

Enterprise model serving decisions involve choosing between managed services (cloud provider ML platforms, API providers), open-source self-hosted solutions, and hybrid approaches. Key considerations include inference latency requirements, throughput needs, cost per prediction, model size and complexity, data privacy constraints, and operational team capability. Production serving systems require comprehensive monitoring of latency distributions, throughput, error rates, model accuracy metrics, and resource utilization to maintain service quality and optimize costs.