Description:

In this assignment, you will deploy a machine learning model as a web application using Flask or FastAPI. The goal is to create a RESTful API that allows users to interact with the model by sending input data and receiving predictions. You will learn how to integrate a trained machine learning model into a web service, handle HTTP requests, and optimize the deployment for performance and scalability.

Key Objectives:

1. Model Integration: Learn how to load and integrate a trained machine learning model into a Flask or FastAPI application.
2. API Development: Develop RESTful API endpoints that handle user inputs, pass them to the model for prediction, and return the results in JSON format.
3. Input Validation: Implement input validation and error handling to ensure robustness and reliability of the service.
4. Deployment Optimization: Optimize the deployment for handling multiple requests efficiently, focusing on performance and scalability.
5. Testing and Documentation: Test the API endpoints, document the API with clear usage instructions, and ensure the model can be accessed and used by external clients.

By the end of this assignment, you will have a working model deployment that can be accessed via HTTP requests, making your machine learning model ready for production use.