Serverless ML Inference with AWS Lambda + Docker

Running ML models in production sounds simple until you realize you're paying for servers 24/7 even when nobody is using them. That was my situation. I had a model running on EC2, serving predictions through Flask. It worked. It also quietly burned money every hour of the day. So I rebuilt the entire inference pipeline using AWS Lambda and reduced costs to almost zero during idle time. This post walks through exactly how I did it. The Problem with "Always-On" ML Inference When I first deployed a machine learning model, I followed the standard approach: Flask API EC2 instance Load model at startup Serve predictions over HTTP It worked. But it also meant: Paying for compute 24/7 Even at 3AM when traffic = 0 For systems like AquaChain, inference is event-driven: Bursts of requests from devices Long idle periods Running a server continuously for this pattern is wasteful. Enter: Serverless ML Inference With AWS Lambda: You pay only when your model runs No idle infrastructure Fully event-dri