Model Serving with FastAPI
Introduction
A trained model sitting in MLflow isn't useful until it can make predictions on new data.
Model serving is how you expose a model as an API that applications can call.
FastAPI is a modern Python web framework that's perfect for model serving: it's fast,
async-friendly, and automatically generates API documentation.
Why This Matters
You've already used FastAPI for the learning app. Now you'll see why it's popular
for ML serving:
- Type hints provide automatic request validation
- Async support handles concurrent prediction requests
- Auto-generated OpenAPI docs make integration easy
- It's Python, so loading ML models is straightforward
Understanding model serving is crucial for MLOps:
- You'll deploy and scale inference services
- You'll configure health checks and monitoring
- You'll handle model loading and updates
- You'll think about latency, throughput, and reliability