Module 6 · FastAPI for ML

Module 6: Pydantic Deep Dive

Structure data validation models with Pydantic BaseModel. Write Field filters, model validators decorators, and export objects to dictionaries and JSON lists.

⏱ 20 Min Read • Author: GenAIWallah Team • Updated: May 2026

Day 11

Pydantic Models and Type Hints

Why this matters

Pydantic Models: Pydantic enforces types at the boundary — critical before passing data to numpy/sklearn.

Pydantic v2 models validate JSON bodies before your handler runs. FastAPI uses them for OpenAPI schemas.

from pydantic import BaseModel, Field, field_validator

class LoanApplication(BaseModel):
    income: float = Field(gt=0, description="Annual income")
    age: int = Field(ge=18, le=100)

    @field_validator("income")
    @classmethod
    def income_realistic(cls, v: float) -> float:
        if v > 10_000_000:
            raise ValueError("income out of range")
        return v

Benefits: auto 422 errors, IDE autocomplete, clear docs for API consumers.

Common mistakes

Blocking the event loop with heavy sync code in async routes.
Returning wrong HTTP status codes (200 on validation failure).
Shipping without request/response models for ML endpoints.

Interview checkpoints

Q: Explain pydantic models in one minute. A: Definition + ML deployment angle.
Q: One FastAPI pitfall? A: Validation, async blocking, or wrong status code.

Practice

Basic: Define Pydantic Models and give an example.
Intermediate: Implement a minimal snippet for Pydantic Models.
Advanced: Break it and read the OpenAPI / error response.

Recap

You can explain pydantic models clearly.
You know one mistake to avoid.
You see how this connects to the next lesson.

Next: Validators

Day 12

Field Constraints and Model Validators

Why this matters

Validators: Validators catch bad inputs early with clear 422 responses instead of silent model errors.

Use Field(...) for constraints and @field_validator for custom rules. Serialize with model_dump().

Validate ML inputs (ranges, categories) before model.predict
Separate request vs response models (PredictionOut)
Never expose internal numpy types in JSON — convert to Python scalars/lists

Trilogy complete: You can design REST APIs, serve sklearn models with FastAPI, containerize with Docker, and enforce contracts with Pydantic.

Common mistakes

Blocking the event loop with heavy sync code in async routes.
Returning wrong HTTP status codes (200 on validation failure).
Shipping without request/response models for ML endpoints.

Interview checkpoints

Q: Explain validators in one minute. A: Definition + ML deployment angle.
Q: One FastAPI pitfall? A: Validation, async blocking, or wrong status code.

Practice

Basic: Define Validators and give an example.
Intermediate: Implement a minimal snippet for Validators.
Advanced: Break it and read the OpenAPI / error response.

Recap

You can explain validators clearly.
You know one mistake to avoid.
You see how this connects to the next lesson.

Next: Back to FastAPI Hub

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