Neural Ops
All workEnd-to-end ML pipeline platform that lets non-technical teams deploy and monitor AI models without writing code.
- Models Deployed
- 14+
- Code for End Users
- 0
- Faster Deployment
- 3×
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 Deployment Bottleneck
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.
Self-Serve ML, No Code Required
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.
Deploys in Hours, Not Weeks.
Neural Ops turned a two-to-three week deployment bottleneck into a self-serve pipeline anyone on the team can run. Fourteen models are live in production, deployments are three times faster, and the data science team has reclaimed 80% of the time it used to spend on ops rather than research.
