FastAPI ML Serving Expert
Architects production-ready ML model serving APIs with FastAPI, covering async request handling, Pydantic v2 validation, model loading patterns, batch inference, streaming responses, health checks, OpenAPI documentation, Docker containerization, and GPU inference optimization.
SupaScore
84.4Best for
- ▸Building production-ready ML inference APIs with FastAPI for real-time model serving
- ▸Implementing GPU-optimized batch inference endpoints with async request handling
- ▸Creating streaming prediction APIs with Pydantic v2 validation and health monitoring
- ▸Containerizing ML models with Docker for scalable deployment and model versioning
- ▸Architecting high-throughput inference services with proper error handling and observability
What you'll get
- ●Complete FastAPI project structure with lifespan patterns, Pydantic v2 schemas, async route handlers, and Docker multi-stage builds
- ●Production-ready inference endpoints with GPU memory management, batch processing queues, and comprehensive health monitoring
- ●Streaming response implementations with proper error handling, request logging middleware, and OpenAPI documentation
Not designed for ↓
- ×Training or fine-tuning ML models (focuses only on serving pre-trained models)
- ×Building general web applications without ML inference requirements
- ×Data preprocessing pipelines or ETL workflows for model training
- ×Frontend development or client-side model deployment
Clear ML serving requirements including model framework, latency targets, throughput needs, input/output formats, and deployment constraints.
Complete FastAPI application architecture with async endpoints, Pydantic schemas, Docker configuration, health checks, and production deployment patterns.
Evidence Policy
Enabled: this skill cites sources and distinguishes evidence from opinion.
Research Foundation: 8 sources (4 official docs, 1 academic, 3 industry frameworks)
This skill was developed through independent research and synthesis. SupaSkills is not affiliated with or endorsed by any cited author or organisation.
Version History
Initial release
Prerequisites
Use these skills first for best results.
Works well with
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Common Workflows
ML Model Production Pipeline
End-to-end workflow from model training through production deployment with FastAPI serving layer and container orchestration
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