REST API serving with FastAPI in MLOps - Mini Project: Build & Apply

REST API serving with FastAPI

📖 Scenario: You are building a simple REST API to serve predictions from a machine learning model. This API will receive input data and return a prediction result. FastAPI is a modern, fast web framework for Python that makes it easy to create APIs.

🎯 Goal: Build a basic REST API using FastAPI that accepts input data via a POST request and returns a prediction result as JSON.

📋 What You'll Learn

Create a FastAPI app instance

Define a Pydantic model for input data validation

Create a POST endpoint '/predict' that accepts input data

Return a JSON response with a prediction message

💡 Why This Matters

🌍 Real World

Serving machine learning model predictions via REST APIs is common in production systems. FastAPI helps build these APIs quickly and efficiently.

💼 Career

Understanding how to serve ML models with APIs is essential for MLOps engineers and data scientists working on deploying models to production.

1

Create FastAPI app and input data model

Import FastAPI and BaseModel from fastapi and pydantic respectively. Create a FastAPI app instance called app. Define a Pydantic model called InputData with one field feature of type float.

MLOps

# Your code here

Hint

Use app = FastAPI() to create the app. Define InputData class inheriting from BaseModel with a feature attribute.

2

Add a POST endpoint for prediction

Add a POST endpoint /predict to the app using the @app.post("/predict") decorator. Define a function predict that takes one parameter input_data of type InputData.

MLOps

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()

class InputData(BaseModel):
    feature: float

# Add POST endpoint /predict below
# Your code here

Hint

Use @app.post("/predict") decorator and define predict function with input_data: InputData parameter.

3

Implement prediction logic

Inside the predict function, create a variable result that multiplies input_data.feature by 2. Return a dictionary with key prediction and value as the result.

MLOps

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()

class InputData(BaseModel):
    feature: float

@app.post("/predict")
def predict(input_data: InputData):
    # Your code here

Hint

Calculate result by multiplying input_data.feature by 2. Return it in a dictionary with key prediction.

4

Run the FastAPI app and test output

Print the string "FastAPI app ready to serve predictions" to confirm the app setup is complete.

MLOps

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()

class InputData(BaseModel):
    feature: float

@app.post("/predict")
def predict(input_data: InputData):
    result = input_data.feature * 2
    return {"prediction": result}

# Print confirmation message
# Your code here

Hint

Use print("FastAPI app ready to serve predictions") to show the message.

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