How do you expose a machine learning model as a REST API? Compare Flask and FastAPI for model serving.

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Exposing a machine learning model as a REST API allows other applications to request predictions over HTTP. The client sends input data in a request, typically as JSON, and the server returns the model's prediction in the response.

The basic architecture involves loading the model when the server starts, defining an endpoint that accepts prediction requests, preprocessing the input data to match the model's expected format, running inference, and returning the result. This pattern enables integration with web applications, mobile apps, and other services regardless of their implementation language.

Flask is a lightweight Python web framework widely used for serving ML models. Its simplicity makes it easy to get started. You define routes using decorators, parse JSON request data, call your model, and return a JSON response. Flask follows a synchronous request model where each request blocks until completion. For CPU-bound inference workloads, this is often acceptable. Flask has extensive documentation and a large ecosystem of extensions.

FastAPI is a modern alternative built on asynchronous foundations. It uses Python type hints to automatically validate request data and generate API documentation. The async support allows handling many concurrent connections efficiently, which matters when inference involves I/O operations like calling external services or when requests may queue behind slow predictions. FastAPI is typically two to three times faster than Flask for the same workload due to its underlying Starlette framework and uvicorn server.

Key differences influence the choice between them.

Performance favors FastAPI for high-throughput scenarios. The async architecture handles concurrent requests better, and the framework itself introduces less overhead.

Development speed can favor Flask for simple use cases because many developers are already familiar with it. FastAPI's automatic documentation and validation provide faster development for more complex APIs.

Type safety in FastAPI catches errors at development time through Pydantic validation. Flask requires manual validation code.

Ecosystem maturity gives Flask an advantage with more third-party extensions and longer track record in production.

For production deployments, consider using application servers like Gunicorn with Flask or uvicorn with FastAPI to handle multiple worker processes. Containerization with Docker packages the model and all dependencies together. Load balancers distribute traffic across multiple instances for scalability.

Both frameworks are viable choices. FastAPI is generally recommended for new ML serving projects due to its performance and modern features, while Flask remains a solid option when simplicity or ecosystem compatibility is prioritized.