Project Overview
A set of RESTful APIs providing real-time machine learning inference with cloud-native scalability.
The Problem
Deploying ML models in production while maintaining low latency and scalability.
The Solution
Built stateless FastAPI services with Dockerized deployments and cloud-native infrastructure.
Architecture
- Stateless FastAPI services
- Docker-based deployments
- GCP infrastructure
Key Challenges
- Inference latency
- Scaling under load
- Deployment consistency
Outcome
Enabled reliable real-time ML inference with predictable performance.