Pydantic Schemas for Request and Response
BaseModel, field validation, type hints, response_model, handling validation errors.
Why Schemas?
Raw dicts are error-prone. Pydantic models define the exact shape of requests and responses, validating types automatically and documenting your API. FastAPI uses them natively.
The BaseModel
Pydantic schemas subclass BaseModel. Each typed attribute becomes a validated field; wrong types are rejected before your code runs.
from pydantic import BaseModel
class PredictRequest(BaseModel):
features: list[float]All lessons in this course
- FastAPI Basics for ML Engineers
- Pydantic Schemas for Request and Response
- Loading and Serving ML Models
- Dockerizing the Model API