Introduction
Seldon MLServer is an open-source inference server designed for efficient, extensible, and production-ready model serving, supporting a wide range of ML frameworks and model types. Part of the broader Seldon ecosystem, MLServer provides a minimalist and highly configurable serving engine that aligns with open standards like KServe V2 Inference Protocol, MLflow, and ONNX Runtime. MLServer is used to deploy lightweight, framework-agnostic model endpoints for low-latency inference, model chaining, and rapid prototyping—without unnecessary complexity.
Key benefits of using MLServer include:
Multi-Framework Model Support: Natively supports scikit-learn, XGBoost, LightGBM, PyTorch, TensorFlow, ONNX, Hugging Face Transformers, and custom Python models through standard runtimes or simple wrappers.
Standards-Compliant Interfaces: Implements the V2 Inference Protocol (also supported by KServe), enabling interoperability and smooth integration into multi-model infrastructures.
Fast and Lightweight: Built with performance and simplicity in mind—ideal for high-throughput or resource-constrained inference workloads.
Flexible Deployment and Extension: Easily extensible via Python hooks for pre/post-processing, custom inference logic, and metadata injection—making it a great fit for evolving model needs.
Production-Ready Observability: Integrates with Prometheus, OpenTelemetry, and standard logging systems to offer visibility into request rates, latencies, and model health.
MLServer is used to power real-time model inference for smaller or modular models, experiment sandboxes, lightweight NLP services, and model components in larger chains or agent flows. It integrates with upstream tools like MLflow for model packaging and registry, and downstream platforms like KServe or Ray Serve for orchestration and routing.
By adopting Seldon MLServer, you can ensure that model serving is lightweight, interoperable, and easily extensible—enabling fast iteration and reliable performance across a wide spectrum of ML use cases.