Context
The system had to balance model quality, delivery speed, and operational reliability without assuming ideal infrastructure conditions. That requirement pushed architecture discipline to the front of the build.
Key decisions
- Ensemble inference was chosen over a single-model pipeline because accuracy gains remained meaningful without making the serving layer opaque.
- Redis was introduced to stabilize response time during traffic spikes instead of compensating later with heavier infrastructure.
- The front-end experience stayed narrow and legible so the product communicated trust before it explained capability.
Outcome
The result is a production-shaped ML system that presents architecture tradeoffs clearly, serves users predictably, and remains easy to evaluate during technical review.