Distributed Model Serving
Replicated inference API
A scalable inference API: a FastAPI model service replicated behind an Nginx gateway, with a versioned model registry.
- Context
- Distributed Systems module · 5 members
- Year
- 2026
- Deployment
- Docker Compose
- Valid responses
- 100%
- Every request in the documented load test returned a valid response: 100 requests, 20 at a time.
- FastAPI replicas
- 2
- Identical copies of the model service, with Nginx as the single entry point in front of them.
01Context
A five-person team project for the Distributed Systems module: turning a trained machine-learning model into an inference service designed to scale horizontally.
02Problem
A single inference server is both a bottleneck and a single point of failure. The goal: an API that scales by adding instances, with every model version tracked.
03Architecture
01Client
Inference requests
02Gateway
Nginx
Single entry point
03Service
FastAPI · instance 1
03Service
FastAPI · instance 2
04Registry
Versioned models
Model registry
04Team
Team project (5 members) focused on scalable model inference, with a FastAPI service replicated across two instances behind an Nginx gateway, Docker Compose deployment and a versioned model registry.
05Technical decisions
- 01
Scale out, not up
The FastAPI service runs as identical replicas, so capacity grows by adding instances rather than a bigger server.
- 02
One gateway
Nginx is the single entry point; clients never address a replica directly.
- 03
Versioned models
A model registry versions every model the service can load.
- 04
Reproducible deployment
Docker Compose defines the gateway and the replicas as one reproducible stack.
06Results
- A complete serving stack, from gateway to model registry, reproducible with Docker Compose
- Load-tested under concurrent requests, with the results documented
07Stack
- Python
- FastAPI
- Nginx
- Docker Compose
- scikit-learn
08Source code
There’s no public repository for this project. Other projects are on GitHub.
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