Day 09 of MLOps: From Localhost to Production-Ready ML Deployment on AWS

2026-06-22 · Source: Machine Learning on Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cloud Computing & IT Infrastructure · Depth: Intermediate, quick

Summary

This blog post details the process of transitioning an intent classification machine learning model from a local development environment to a production-ready deployment on Amazon Web Services (AWS). It outlines how to achieve high availability, scalability, load balancing, automated recovery, and infrastructure automation. The deployment leverages AWS services including EC2, Auto Scaling Groups, and Application Load Balancers, alongside infrastructure-as-code tool Terraform. Furthermore, the guide explains the roles of NGINX as a reverse proxy, Gunicorn for serving Python applications, and Systemd for service management, all crucial components for reliable production ML systems.

Key takeaway

For MLOps Engineers scaling machine learning models, this guide provides a clear path to production readiness on AWS. You should adopt infrastructure-as-code with Terraform and integrate components like Gunicorn, NGINX, and Systemd to ensure your Flask ML applications are highly available, scalable, and resilient. This approach moves beyond basic API deployment to robust, automated operationalization.

Key insights

Production ML deployment on AWS requires specific tools and practices for scalability, reliability, and automation beyond localhost.

Principles

• Production deployments need high availability, scalability, load balancing, automated recovery, and infrastructure automation.
• WSGI is crucial for Python application deployments.

Method

Deploy a Flask ML application using Gunicorn, configure NGINX as a reverse proxy, run as a Systemd service, provision AWS infrastructure with Terraform, and deploy behind an Auto Scaling Group and Load Balancer.

In practice

• Use Gunicorn to serve Flask ML applications.
• Implement NGINX for reverse proxying.
• Manage services with Systemd.

Topics

• MLOps
• AWS EC2
• ML Model Deployment
• Terraform
• Auto Scaling Groups
• NGINX
• Gunicorn

Best for: MLOps Engineer, Machine Learning Engineer, AI Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Machine Learning on Medium.