# Ml Serving Frameworks > Deploying models as high-performance, scalable microservices using specialized serving frameworks. Use when this capability is needed. - Skill: `tomevault-io/ml-serving-frameworks` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add tomevault-io/ml-serving-frameworks` - Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/ml-serving-frameworks/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: tomevault-io (https://skillmd.com/u/tomevault-io) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/tomevault-io/ml-serving-frameworks --- # ML Serving Frameworks Moving a model from a notebook to production requires more than just an API endpoint. ## Key Challenges - **Dynamic Batching**: Grouping requests to maximize GPU/CPU throughput. - **Model Graphing**: Chaining pre-processing, inference, and post-processing steps. - **Autoscaling**: Adjusting resources based on traffic. ## Frameworks - **BentoML**: Packaging models as OCI-compliant containers. - **Ray Serve**: A scalable model serving library built on Ray. - **FastAPI**: For simple, lightweight model microservices. ## Best Practices - **Inference Optimization**: Use ONNX or TensorRT to accelerate predictions. - **Monitoring**: Track latency, throughput, and system health (CPU/Memory). --- > Converted and distributed by [TomeVault](https://tomevault.io/claim/jcorpac) — claim your Tome and manage your conversions.