Home▸AI & Machine Learning▸Packaging Machine Learning Models with FastAPI and Docker for Production

🐳 Packaging Machine Learning Models with FastAPI and Docker for Production

An interactive 3D deployment pipeline: requests flow from a client through FastAPI request validation into a fleet of Docker containers serving a model, with a live rolling update that swaps versions with zero downtime.

AI & Machine Learning3DAdvanced60 FPS

Requests flow from a client through a FastAPI validation gate into a fleet of Docker containers serving a packaged model, and a rolling update swaps container versions one at a time while traffic keeps flowing.

🔬 What It Demonstrates

FastAPI validates every request against a schema before it reaches a model container; malformed payloads bounce back as a 422 instead of reaching — and wasting — compute. Traffic is load-balanced across replicas, and a rolling update replaces containers one at a time so the service never goes down.

🎮 How to Use

Raise the request rate to load the pipeline, add replicas to spread that load, and increase invalid payloads to see the validation gate at work. Click "Roll out v-next" to watch a zero-downtime rolling deployment move through the fleet.

💡 Did You Know?

Because a container image bundles the exact Python version, libraries, and model weights together, "works on my machine" mostly disappears — the same image that passed tests is the one that serves production traffic.

⚙ Under the hood

machine learningfastapidockerproduction deploymentmodel servingzero downtimeThree.js

3D · Three.js / WebGL renderer · 60 FPS target · runs fully client-side, no install