REST API serving with FastAPI in MLOps - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is FastAPI used for in REST API serving?

FastAPI is a modern, fast web framework for building REST APIs with Python. It helps create APIs quickly with automatic data validation and documentation.

intermediate

How does FastAPI handle data validation?

FastAPI uses Python type hints and Pydantic models to automatically validate and convert incoming request data, ensuring the API receives the correct data types.

beginner

What command starts a FastAPI server using Uvicorn?

The command is: uvicorn main:app --reload. Here, 'main' is the Python file name and 'app' is the FastAPI instance. '--reload' restarts the server on code changes.

intermediate

Why is automatic API documentation useful in FastAPI?

FastAPI generates interactive API docs (Swagger UI and ReDoc) automatically, making it easy to test and understand API endpoints without extra setup.

beginner

What is the role of path operation functions in FastAPI?

Path operation functions define how the API responds to HTTP requests at specific paths and methods (GET, POST, etc.). They contain the logic for each API endpoint.

Which Python library does FastAPI use for data validation?

A Pydantic

B NumPy

C Requests

D Flask

FastAPI uses Pydantic to validate and parse data automatically based on Python type hints.

What does the '--reload' option do when running Uvicorn with FastAPI?

A Restarts the server on code changes

B Enables debug mode

C Runs the server in production mode

D Disables logging

The '--reload' option makes the server restart automatically when code changes, useful during development.

Which HTTP method is used to retrieve data in a REST API?

A POST

B DELETE

C PUT

D GET

GET requests are used to retrieve data from a REST API.

What is the default port Uvicorn uses when starting a FastAPI app?

A 5000

B 8000

C 8080

D 3000

Uvicorn defaults to port 8000 when starting a FastAPI server.

Which feature of FastAPI helps generate interactive API docs automatically?

A Flask Admin

B Django REST Framework

C Swagger UI

D Jinja2

FastAPI integrates Swagger UI to provide interactive API documentation automatically.

Explain how FastAPI uses Python type hints and Pydantic models to serve REST APIs.

Think about how FastAPI checks incoming data before running your code.

Describe the steps to start a FastAPI server and test an endpoint.

Focus on writing code, running server, and checking API docs.

Practice

1. What is the main purpose of using FastAPI in serving machine learning models?

easy

A. To train machine learning models faster

B. To create fast and simple REST APIs for model serving

C. To store large datasets efficiently

D. To visualize model performance graphs

Solution

1. Step 1: Understand FastAPI's role

   FastAPI is a web framework used to build APIs quickly and simply.

2. Step 2: Connect to model serving

   It is commonly used to serve machine learning models via REST APIs for easy access.

3. Final Answer:

   To create fast and simple REST APIs for model serving -> Option B

4. Quick Check:

   FastAPI = REST API serving [OK]

Hint: FastAPI is for building APIs, not training or storage [OK]

Common Mistakes:

• Confusing API serving with model training
• Thinking FastAPI handles data storage
• Assuming FastAPI is for visualization

2. Which of the following is the correct way to define a GET endpoint in FastAPI?

easy

A. @app.route('/items', method='GET')
def read_items():
return {'items': []}

B. @app.post('/items')
def read_items():
return {'items': []}

C. @app.get('/items')
def read_items():
print('items')

D. @app.get('/items')
def read_items():
return {'items': []}

Solution

1. Step 1: Identify correct decorator for GET

   FastAPI uses @app.get() to define GET endpoints.

2. Step 2: Check function returns JSON response

   The function should return a dictionary to send JSON; print statement does not return data.

3. Final Answer:

   @app.get('/items')\ndef read_items():\n return {'items': []} -> Option D

4. Quick Check:

   @app.get() + return dict = correct GET endpoint [OK]

Hint: Use @app.get() and return dict for GET endpoints [OK]

Common Mistakes:

• Using @app.post() for GET endpoints
• Using print instead of return
• Using Flask-style @app.route() syntax

3. What will be the output when you call the following FastAPI endpoint?

@app.get('/hello')
def say_hello():
    return {'message': 'Hello, FastAPI!'}

medium

A. {\"message\": \"Hello, FastAPI!\"}

B. Hello, FastAPI!

C. Error: Missing return type

D. 404 Not Found

Solution

1. Step 1: Analyze endpoint return value

   The function returns a dictionary with key 'message' and value 'Hello, FastAPI!'.

2. Step 2: Understand FastAPI response format

   FastAPI automatically converts dict to JSON response with the same structure.

3. Final Answer:

   {"message": "Hello, FastAPI!"} -> Option A

4. Quick Check:

   Return dict = JSON response with same keys [OK]

Hint: Return dict from endpoint gives JSON response [OK]

Common Mistakes:

• Expecting plain string instead of JSON
• Thinking missing return type causes error
• Assuming endpoint path is incorrect

4. Identify the error in this FastAPI POST endpoint code:

@app.post('/predict')
def predict(data: dict):
    return {'result': data['value'] * 2}

medium

A. Missing request body declaration with Pydantic model

B. Incorrect HTTP method, should be GET

C. Function missing return statement

D. Syntax error in decorator

Solution

1. Step 1: Check parameter type for POST data

   FastAPI requires request body to be declared with Pydantic models or Body for parsing JSON.

2. Step 2: Understand why dict alone is insufficient

   Using plain dict as parameter does not parse JSON body automatically, causing validation error.

3. Final Answer:

   Missing request body declaration with Pydantic model -> Option A

4. Quick Check:

   POST body needs Pydantic model or Body [OK]

Hint: Use Pydantic models for POST request bodies [OK]

Common Mistakes:

• Using plain dict instead of Pydantic model
• Confusing POST with GET method
• Forgetting to return a response

5. You want to serve a machine learning model prediction via FastAPI. Which approach correctly handles input validation and prediction?

from pydantic import BaseModel

class InputData(BaseModel):
    feature1: float
    feature2: float

@app.post('/predict')
def predict(data: InputData):
    result = model.predict([[data.feature1, data.feature2]])
    return {'prediction': result[0]}

What is the main advantage of this design?

hard

A. It skips input validation for faster response

B. It trains the model on each request

C. It validates input data types automatically before prediction

D. It returns raw model object instead of prediction

Solution

1. Step 1: Understand Pydantic model role

   InputData class validates that feature1 and feature2 are floats before function runs.

2. Step 2: Connect validation to prediction safety

   This prevents invalid data from reaching model.predict, avoiding runtime errors.

3. Final Answer:

   It validates input data types automatically before prediction -> Option C

4. Quick Check:

   Pydantic = automatic input validation [OK]

Hint: Use Pydantic models to validate inputs before prediction [OK]

Common Mistakes:

• Thinking model retrains on each request
• Skipping validation causes errors
• Returning model object instead of prediction