Beginner’s Guide to Deploying a Machine Learning API with FastAPI

In this guide, you will learn how to deploy a machine learning model as an API using FastAPI. We will create an API that predicts the species of a penguin based on its bill length and flipper length.

Prerequisites

• Basic knowledge of Python
• Python installed on your system (preferably version 3.7 or higher)
• Familiarity with machine learning concepts (optional)

Step 1: Set Up Your Environment

1. Create a Project DirectoryOpen your terminal and create a new directory for your project:
2. Set Up a Virtual EnvironmentCreate and activate a virtual environment:
3. On windows use: venvScriptsactivate
4. Install Required PackagesInstall FastAPI, Uvicorn (for serving the app), and other necessary libraries:

Step 2: Prepare Your Machine Learning Model

1. Download DatasetFor this example, we will use the Palmer Penguins dataset. You can download it from here.
2. Create a Python Script for the ModelCreate a file named model.py in your project directory:

Step 3: Create the FastAPI Application

1. Create the Main Application FileCreate a file named main.py:

Step 4: Run Your FastAPI Application

1. Run the Application In your terminal, run the following command:

2. Access the API Open your web browser and navigate to http://127.0.0.1:8000/docs. This will open Swagger UI, where you can test your API.

Step 5: Test Your API

1. Use Swagger UIIn the Swagger UI, find the /predict endpoint, click on it, and then click “Try it out.” Enter values for bill_length and flipper_length, then click “Execute.” You should see a response with the predicted penguin species!

Conclusion

Congratulations! You have successfully deployed a machine learning API using FastAPI. This guide covered:

• Setting up your environment.
• Preparing a machine learning model.
• Creating a FastAPI application.
• Running and testing your API.

Next Steps

• Explore more advanced features of FastAPI like authentication and database integration.
• Experiment with different machine learning models and datasets.
• Consider containerizing your application using Docker for easier deployment.

Feel free to reach out if you have any questions or need further assistance!

Nikhil is an intern consultant at Marktechpost. He is pursuing an integrated dual degree in Materials at the Indian Institute of Technology, Kharagpur. Nikhil is an AI/ML enthusiast who is always researching applications in fields like biomaterials and biomedical science. With a strong background in Material Science, he is exploring new advancements and creating opportunities to contribute.