The fusion of machine learning and web technologies is revolutionizing industries, enabling real-time predictions and interactive applications. Deploying such solutions, however, requires robust backend support and scalable infrastructure. In this post, we explore the deployment of a Flask-based ML application on AWS EC2, showcasing both manual and automated approaches.

“Every deployment process is carefully structured to ensure the application performs efficiently and reliably in production environments.”

A key aspect of this deployment was integrating Nginx as a reverse proxy and Gunicorn as the WSGI server. These tools provide a robust architecture for handling concurrent requests, making the application production-ready.

The Flask ML App

The application predicts salaries based on user input using a pre-trained ML model. Built on Python's Flask framework, it serves as an intuitive interface for the model while maintaining lightweight and scalable functionality.

Key Deployment Features

  • Gunicorn: A Python WSGI server optimized for handling multiple requests.
  • Nginx: A high-performance reverse proxy for routing requests efficiently.
  • AWS EC2: Scalable infrastructure ensuring reliability under variable loads.

Data Preparation and App Design

Before deployment, the ML model underwent rigorous training on salary data to ensure accurate predictions. Preprocessing steps, including scaling and feature selection, ensured that the model was both efficient and generalizable.

“From feature engineering to endpoint design, every detail was optimized for real-world usability.”

The trained model was exported as a pickle file, ready to be integrated with the Flask backend. The application was designed with endpoints to accept user input, process it through the ML model, and return predictions seamlessly.

Deployment Process

Manual Deployment

1. Setting Up the Environment: The EC2 instance was configured with the required packages, including Python, Flask, Gunicorn, and Nginx.

2. Creating a Virtual Environment: A Python virtual environment ensured consistent package management, isolating dependencies.

3. Configuring Gunicorn: Gunicorn was set up to run the Flask app efficiently on localhost, binding to port 8000.

4. Nginx as Reverse Proxy: Nginx routed external requests to Gunicorn, enabling a secure and optimized communication layer.

5. Testing the Application: End-to-end testing confirmed accurate predictions and accessibility via the EC2 public IP.

Automated Deployment

An AWS CloudFormation stack streamlined the deployment process, automating the creation of the EC2 instance and essential configurations. This reduced manual effort, allowing developers to focus on app refinement.

Model Deployment and Validation

Handling Requests

The Flask app was designed to handle concurrent requests efficiently. Real-time input validation ensured that users received accurate predictions within milliseconds.

Performance Optimization

  • Gunicorn’s worker processes distributed the load effectively.
  • Nginx caching mechanisms minimized redundant processing.

“Rigorous testing and validation ensured the application met production-grade performance benchmarks.”

The Final Result

The deployed application is accessible through the EC2 instance’s public IP, ready to deliver real-time salary predictions. Whether you're experimenting with manual deployment or embracing automation, the process equips you with essential skills for scaling ML applications in the cloud.