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.