Deploying Models as REST APIs

Welcome back! So far, you have learned how to explore and prepare data, train a linear regression model, and evaluate its performance on unseen data. In the previous lesson, you saw how to use your trained model to make predictions and assess how well it generalizes to new situations.

Now, it is time to take the next big step: making your model available for others to use. In real-world applications, machine learning models are rarely used only by the person who trained them. Instead, they are often deployed as web services so that other applications, websites, or users can send data and receive predictions. This is where REST APIs come in. In this lesson, you will learn how to deploy your trained model with a REST API using FastAPI. By the end of this lesson, you will know how to build, run, and test an API that serves predictions from your model, making your work accessible and useful in real-world scenarios.

Before we dive into the code, let's briefly discuss what a REST API is and why it is important in machine learning deployment. A REST API (Representational State Transfer Application Programming Interface) is a way for different software systems to communicate over the web using standard HTTP methods like GET and POST. When you deploy your model as a REST API, you make it possible for other programs to send data to your model and receive predictions in return, all through simple web requests.

FastAPI is a modern Python web framework designed for building APIs quickly and efficiently. It is known for its speed, ease of use, and automatic documentation features. FastAPI also makes it easy to handle errors, which is important for building reliable machine learning services. By using FastAPI, you can create a scalable and production-ready API with minimal code.

The first step in building your API is to load the trained model that you saved in previous lessons. You will use the joblib library to load the model from the joblib file:

import joblib

# Load the trained model
model = joblib.load('trained_model.joblib')

This loads your trained linear regression model into memory so it can be used to make predictions when requests come in.