{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/8"}],"enrichment":{"capability":"MLServer is an open-source inference server that exposes machine learning models through REST and gRPC APIs compliant with the KFServing V2 Dataplane specification, supporting multi-model serving, adaptive batching, and parallel inference.","skillfed_tags":["model-serving","inference-server","kubernetes"],"use_cases":["Serve multiple scikit-learn or XGBoost models from a single process with REST/gRPC endpoints.","Deploy models to Kubernetes using Seldon Core or KServe with MLServer as the core Python backend.","Implement adaptive batching to group inference requests and improve throughput for batch-friendly models.","Monitor and trace inference requests using OpenTelemetry integration for observability.","Build custom inference logic by writing a custom runtime and registering it with MLServer."],"what_it_does":"MLServer is a production-grade inference server that wraps machine learning models and exposes them via standardized REST and gRPC endpoints. It implements the KFServing V2 Dataplane protocol, allowing models to be served in a framework-agnostic way and integrated into Kubernetes-native platforms like Seldon Core and KServe. The server handles multi-model serving within a single process, supports adaptive batching to group requests on the fly, and can parallelize inference across worker pools for vertical scaling.\n\nThe package comes with 25 runtime dependencies covering FastAPI for HTTP handling, gRPC for RPC, OpenTelemetry for observability, Kafka for event streaming, and Pydantic for validation. It ships with pre-packaged runtimes for common frameworks (scikit-learn, XGBoost, LightGBM, HuggingFace, etc.), but you can also write custom runtimes. Installation is straightforward, though you must separately install framework-specific runtime packages to serve models from those frameworks.","worth_installing":"Yes, with conditions. MLServer is suitable for production model serving if you need KFServing V2 compliance, multi-model serving, or Kubernetes integration. However, the aging maintenance status warrants checking whether security patches and bug fixes align with your production requirements. Evaluate the 25 dependencies for your deployment footprint and confirm compatibility with Python 3.9\u20133.12. No known vulnerabilities are reported."},"id":"mlserver","links":{"html":"https://skillfed.io/packages/mlserver","md":"https://skillfed.io/packages/mlserver.md","pypi":"https://pypi.org/project/mlserver/"},"maintenance":{"status":"aging"},"meta":{"latest_release":"2025-06-06","license_spdx":null,"license_treatment":"permissive","name":"mlserver","python_support":"capped_below_current","summary":"MLServer"},"popularity":{"monthly_downloads":166120,"position":10509,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.7.1"}