Model Serving

Operationalize trained ML models: deploy them as scalable, low-latency REST or gRPC API endpoints for real-time inference.

Model Serving is the critical MLOps step that transitions a trained artifact into a production-ready, network-invokable service. It exposes your prediction logic via a high-performance API (e.g., REST on port 8501 or gRPC on port 8500), enabling applications like e-commerce recommendation engines to get real-time inference. Key platforms—such as KServe or TensorFlow Serving—manage essential production requirements: dynamic scaling, version control (for A/B testing), and low-latency throughput for millions of requests. This architecture eliminates monolithic deployments, centralizing your ML assets for efficient, multi-application use.

https://www.tensorflow.org/serving