FastAPI for model serving in ML Python

Introduction

FastAPI helps you quickly share your machine learning model so others can use it easily through the internet.

You want to let a website or app get predictions from your model.

You need to test your model with real data from users.

You want to share your model with teammates without sending code.

You want to build a simple API to get model results anytime.

You want to deploy your model on a server for others to access.

Syntax

ML Python

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()

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

@app.post('/predict')
async def predict(data: InputData):
    # Use your model here
    prediction = data.feature1 + data.feature2
    return {'prediction': prediction}

Use @app.post to create a POST endpoint where you send data.

Define input data with BaseModel to check data types automatically.

Examples

A simple GET endpoint that returns a welcome message.

ML Python

from fastapi import FastAPI

app = FastAPI()

@app.get('/')
async def root():
    return {'message': 'Hello World'}

Example of a POST endpoint that calculates a score from input features.

ML Python

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()

class InputData(BaseModel):
    age: int
    income: float

@app.post('/predict')
async def predict(data: InputData):
    score = data.age * 0.1 + data.income * 0.01
    return {'score': score}

Sample Model

This program creates a FastAPI app with one POST endpoint '/predict'. It takes two numbers, adds them, and returns the result as prediction.

Run this code, then send a POST request with JSON like {"feature1": 3.5, "feature2": 2.5} to get prediction 6.0.

ML Python

from fastapi import FastAPI
from pydantic import BaseModel
import uvicorn

app = FastAPI()

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

@app.post('/predict')
async def predict(data: InputData):
    # Simple model: sum of features
    prediction = data.feature1 + data.feature2
    return {'prediction': prediction}

if __name__ == '__main__':
    uvicorn.run(app, host='127.0.0.1', port=8000)

Important Notes

Use tools like curl or Postman to send POST requests to your FastAPI server.

FastAPI automatically creates interactive docs at /docs to test your API.

Keep your model loading outside the endpoint function to avoid reloading on every request.

Summary

FastAPI makes it easy to share your ML model as a web service.

Define input data clearly with Pydantic models for safety and clarity.

Use POST endpoints to send data and get predictions back in JSON format.

Practice

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

easy

A. To create a web service that allows users to send data and get model predictions

B. To train machine learning models faster on GPUs

C. To visualize data with interactive charts

D. To store large datasets efficiently

Solution

1. Step 1: Understand FastAPI's role

   FastAPI is a web framework used to build APIs that can serve machine learning models.

2. Step 2: Identify the main use in ML serving

   It allows sending input data and receiving predictions via HTTP requests, making models accessible.

3. Final Answer:

   To create a web service that allows users to send data and get model predictions -> Option A

4. Quick Check:

   FastAPI serves ML models as web APIs = D [OK]

Hint: FastAPI is for serving models, not training or visualization [OK]

Common Mistakes:

• Confusing FastAPI with training libraries
• Thinking FastAPI is for data storage
• Assuming FastAPI is for data visualization

2. Which of the following is the correct way to define a POST endpoint in FastAPI for model prediction?

easy

A. app.post('/predict')

B. app.put('/predict')

C. app.get('/predict')

D. app.delete('/predict')

Solution

1. Step 1: Recall HTTP methods for sending data

   POST is used to send data to the server, suitable for prediction requests.

2. Step 2: Match method to FastAPI syntax

   FastAPI uses @app.post decorator to define POST endpoints.

3. Final Answer:

   app.post('/predict') -> Option A

4. Quick Check:

   POST endpoint for sending data = app.post [OK]

Hint: Use POST to send data for predictions in FastAPI [OK]

Common Mistakes:

• Using GET instead of POST for sending input data
• Confusing HTTP methods like PUT or DELETE
• Missing the @ decorator syntax

3. Given the following FastAPI code snippet, what will be the output when sending JSON {"text": "hello"} to /predict?

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()

class InputData(BaseModel):
    text: str

@app.post('/predict')
def predict(data: InputData):
    return {"length": len(data.text)}

medium

A. {"text": 5}

B. {"length": "hello"}

C. {"length": 5}

D. Error: Missing return type

Solution

1. Step 1: Understand input and output

   The input JSON has "text": "hello". The function returns the length of this string.

2. Step 2: Calculate length of 'hello'

   "hello" has 5 characters, so the output JSON is {"length": 5}.

3. Final Answer:

   {"length": 5} -> Option C

4. Quick Check:

   len('hello') = 5 [OK]

Hint: Count characters in input string for length output [OK]

Common Mistakes:

• Returning the string instead of its length
• Confusing keys in the output JSON
• Expecting a syntax or runtime error

4. Identify the error in this FastAPI model serving code:

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()

class InputData(BaseModel):
    value: int

@app.post('/predict')
def predict(data: InputData):
    return {"result": data.value * 2}

# Client sends JSON: {"value": "10"}

medium

A. Missing return statement

B. Type error because 'value' should be int but client sent string

C. Syntax error in function definition

D. No error, code works fine

Solution

1. Step 1: Check data type in Pydantic model

   InputData expects 'value' as int, client sends string "10".

2. Step 2: Understand Pydantic validation and coercion

   Pydantic automatically converts compatible strings like "10" to int 10, so no validation error occurs.

3. Final Answer:

   No error, code works fine -> Option D

4. Quick Check:

   Pydantic coerces "10" to 10 [OK]

Hint: Pydantic automatically converts compatible types like str to int [OK]

Common Mistakes:

• Thinking Pydantic doesn't auto-convert string to int
• Expecting a type validation error
• Overlooking Pydantic's coercion feature

5. You want to serve a sentiment analysis model using FastAPI. The model expects a list of texts and returns a list of sentiment scores. Which is the best way to define the input and output in FastAPI?

hard

A. Accept raw JSON string and return a plain string

B. Use a Pydantic model with a field 'texts' as List[str] and return List[float]

C. Use query parameters for each text input and return a single float

D. Use a Pydantic model with a single string field and return a dictionary with one score

Solution

1. Step 1: Define input for multiple texts

   Since the model expects multiple texts, input should be a list of strings, best defined with Pydantic List[str].

2. Step 2: Define output for multiple scores

   The model returns multiple sentiment scores, so output should be a list of floats matching input size.

3. Final Answer:

   Use a Pydantic model with a field 'texts' as List[str] and return List[float] -> Option B

4. Quick Check:

   Input/output types match model expectations [OK]

Hint: Match input/output types to model's batch processing [OK]

Common Mistakes:

• Using single string input for multiple texts
• Returning single score instead of list
• Using query params for large input lists