Validate Requests with Pydantic
Reject malformed input before it reaches the model.
Validate Requests with Pydantic is a free MLOps Academy lesson on CoddyKit — lesson 2 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the MLOps Academy learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.
Garbage In, Garbage Out
If a client sends the wrong fields or types, your model gets nonsense. You want to reject bad input before it ever reaches the model. 🛡️
Meet Pydantic
FastAPI uses Pydantic to validate requests. You describe the shape of the data with a Python class, and validation happens for free.
Define a Schema
You subclass BaseModel and list the fields with their types. This class becomes the contract for your /predict request body.
from pydantic import BaseModel
class IrisInput(BaseModel):
sepal_length: float
petal_width: floatUse It in the Route
You type-annotate the endpoint argument with your model. FastAPI parses, validates, and hands you a clean object.
@app.post("/predict")
def predict(data: IrisInput):
...Access the Fields
Inside the function the validated data is a normal object. You read each value with simple dot access.
x = [data.sepal_length, data.petal_width]
pred = model.predict([x])Automatic 422 Errors
If a field is missing or has the wrong type, FastAPI returns a 422 response with a clear message. You write zero error-handling code.
Add Value Constraints
You can enforce ranges with Field. Here a measurement must be greater than zero, so negatives are rejected automatically.
from pydantic import Field
sepal_length: float = Field(gt=0)Validate a List of Rows
To score many samples at once, you accept a list of your model. Pydantic validates every item in the batch for you.
@app.post("/predict")
def predict(rows: list[IrisInput]):
...Document Fields
Add descriptions and examples to fields, and they show up in the /docs page so callers know exactly what to send. 📘
sepal_length: float = Field(description="cm", examples=[5.1])Shape the Response Too
You can declare a response_model so the output is validated and documented just like the input, keeping your API contract tight.
@app.post("/predict", response_model=Prediction)
def predict(data: IrisInput):
...Why This Matters
Strong input validation is your first line of defense in production. It blocks malformed requests so the model only ever sees clean, expected data. ✅
Quick Check
A client sends a request missing a required field. What does FastAPI do thanks to Pydantic?
Recap
You defined a BaseModel schema, typed your route with it, added field constraints, and let FastAPI auto-reject bad input with clear 422 errors. Clean data in! 🙌
Frequently asked questions
Is the “Validate Requests with Pydantic” lesson free?
Yes — the full text of “Validate Requests with Pydantic” is free to read here on the web, and the MLOps Academy course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the MLOps Academy course, upgrade to CoddyKit PRO.
What will I learn in “Validate Requests with Pydantic”?
Reject malformed input before it reaches the model. You practise MLOps Academy with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.
Do I need any experience to start MLOps Academy?
No prior experience is required. MLOps Academy on CoddyKit is structured for beginners through advanced learners; this is — lesson 2 of 4, so you can start here or from the beginning and move at your own pace.
How long does the “Validate Requests with Pydantic” lesson take?
Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.
Can I write and run code in this MLOps Academy lesson?
Yes. Every MLOps Academy lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.
All lessons in this course
- Your First /predict Endpoint
- Validate Requests with Pydantic
- Load the Model Once at Startup
- Add a /health Readiness Check