Giving non-technical teams the power of ML
Neural Ops lets product managers, analysts, and operations teams deploy, monitor, and retrain ML models without writing a single line of code. We built the Python backend, the orchestration layer, the monitoring infrastructure, and the React interface from scratch over 9 months.
The data science team was a bottleneck. Every model deployment required a ticket, a sprint slot, and 2–3 weeks of wait time. The DS team spent 60% of their time on ops rather than research and modelling.
A drag-and-drop pipeline builder lets non-technical users configure model inputs, schedule retraining, and monitor output drift. FastAPI + Celery + Redis on the backend. MLflow for experiment tracking. React frontend with real-time WebSocket updates.
Execution
Mapped every step of the DS team's deploy workflow. Found 11 manual handoffs and 3 approval bottlenecks.
FastAPI + Celery + Redis + PostgreSQL. MLflow for experiment tracking. Docker + Kubernetes for model isolation.
Drag-and-drop builder designed in 4 prototype rounds with product managers as primary test users.
Real-time monitoring dashboard. Live WebSocket inference counts. Custom drift detection visualisations.
Piloted with 2 teams. Iterated on the permissions model. Rolled out company-wide in month 3.
Monthly model health reviews. Automatic alerting when output drift exceeds configurable thresholds.