Python for AI
Intermediate
4.5

Serve an ML Model with FastAPI

Wrap a trained model in a production-ready HTTP endpoint.

1h 40m
1 lesson
1.2K students

What You'll Learn

Learning objectives will be added soon.

Tutorial Content

A minimal prediction API

from fastapi import FastAPI
from pydantic import BaseModel
import joblib

model = joblib.load("model.joblib")
app = FastAPI()

class Input(BaseModel):
    features: list[float]

@app.post("/predict")
def predict(inp: Input):
    pred = model.predict([inp.features])[0]
    return {"prediction": float(pred)}

Run it with uvicorn main:app --reload.

Production checklist

  • Validate inputs with Pydantic (done above).
  • Add a /health endpoint for load balancers.
  • Load the model once at startup, not per request.
  • Containerize with Docker so the environment is reproducible.

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Tags

Python
API
MLOps