Deploying a Machine Learning Model Using Flask, Gunicorn, and Nginx on AWS

Deploying a machine learning model using Flask on a cloud server is a crucial step towards making your application accessible and scalable in a production environment. In this blog post, we walked through the process of deploying a sentiment analysis model using Flask, Gunicorn, and Nginx on an AWS EC2 instance.

Starting with setting up an AWS EC2 instance and SSH-ing into the server, we then deployed our Flask application, created a WSGI file, configured Gunicorn, and set up a systemd service for automatic startup. We also installed and configured NGINX as a reverse proxy server to handle incoming requests efficiently. Finally, we discussed further steps to secure the application by enabling HTTPS using Let’s Encrypt.

By following the steps outlined in this post, you can successfully deploy your Flask application on a cloud server, ensuring that your machine learning model is accessible and scalable for real-world use. With Flask handling the application layer, Gunicorn managing multiple requests efficiently, and NGINX serving as a reverse proxy, your application is well-equipped to handle production workloads.

Remember, deploying a machine learning model is just the beginning. Continuous monitoring, maintenance, and improvements are essential to ensure optimal performance and user experience. By leveraging the power of Flask, Gunicorn, and NGINX, you can create a robust and secure environment for your machine learning applications.

Stay tuned for more insights and best practices on deploying machine learning models and building scalable applications. Happy coding!