FastAPI for model serving in ML Python - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is FastAPI used for in machine learning?

FastAPI is used to create web APIs that serve machine learning models, allowing other applications to send data and get predictions quickly and easily.

intermediate

How does FastAPI handle incoming data for model predictions?

FastAPI uses Python type hints and Pydantic models to validate and parse incoming data automatically before passing it to the machine learning model.

beginner

What is the role of the @app.post decorator in FastAPI model serving?

The @app.post decorator defines an endpoint that accepts POST requests, which is commonly used to send input data to the model and receive predictions.

intermediate

Why is asynchronous programming useful in FastAPI when serving models?

Asynchronous programming allows FastAPI to handle multiple requests at the same time without waiting for each model prediction to finish, improving speed and responsiveness.

beginner

What is a simple way to test a FastAPI model serving endpoint?

You can use tools like curl, Postman, or the interactive Swagger UI automatically provided by FastAPI to send test data and see the model's prediction response.

Which HTTP method is commonly used to send data to a FastAPI model serving endpoint?

A POST

B GET

C DELETE

D PUT

POST is used to send data to the server, which is typical for model prediction requests.

What Python library does FastAPI use to validate incoming request data?

A NumPy

B Pydantic

C Pandas

D Matplotlib

Pydantic is used by FastAPI to validate and parse data based on Python type hints.

What feature of FastAPI helps you automatically get API documentation?

A TensorBoard

B Jupyter Notebook

C Flask

D Swagger UI

FastAPI automatically generates interactive API docs using Swagger UI.

Why might you use async functions in FastAPI model serving?

A To handle multiple requests efficiently

B To slow down the server

C To avoid using Python

D To make the code longer

Async functions let FastAPI handle many requests at once without waiting for each to finish.

Which of these is NOT a typical step in serving a model with FastAPI?

A Load the trained model

B Define an API endpoint

C Train the model inside the API endpoint

D Return predictions as a response

Training the model inside the API endpoint is inefficient; models are trained beforehand and only used for prediction in FastAPI.

Explain how FastAPI helps in serving a machine learning model to users.

Think about how data flows from user to model and back.

Describe the benefits of using asynchronous endpoints in FastAPI for model serving.

Consider what happens when many users ask for predictions at the same time.

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