Deployment & APIs
I turn models and prototypes into usable systems by packaging them behind clean APIs, building lightweight web interfaces, and deploying them on devices or servers with stable performance.
Outcomes
- Stable API endpoints for inference, reporting, and integrations
- Low-latency deployments on edge devices or servers
- Simple web interfaces for non-technical users
- Versioned models and configs so updates are controlled
- Clear documentation and handover for maintainability
Common use cases
Model served as an API
Expose prediction endpoints with clear schemas, validation, and documentation.
Edge inference deployment
Run models locally on Raspberry Pi or similar devices with performance tuning.
Web UI for predictions
Provide an upload form, live results, and visual outputs like bounding boxes.
Real-time integrations
Connect ingestion and dashboards using websockets or SignalR style updates.
How I work
- Clarify target environment and success metrics (latency, throughput, reliability)
- Define API contracts and integration requirements
- Package core logic into a clean service layer
- Implement endpoints, validation, and error handling
- Optimize performance (caching, batching, model warmup)
- Deliver deployment guide and runbook
What you get
- Deployed service (FastAPI or suitable stack) with documented endpoints
- Optional lightweight web UI for usage and demonstration
- Versioned model files and configuration strategy
- Deployment guide (local, edge, or server)
- Basic monitoring/logging setup guidance
Featured Projects
VialVision
An edge-AI computer vision system using YOLO on Raspberry Pi to detect and classify vial conditions.
Used Car Price Prediction
Deployed a machine learning model via REST API to predict car prices based on features.
Volto - Energy Monitoring
Implemented a real-time electricity usage monitoring dashboard with forecasting and anomaly detection.
Ready to talk?
Email me what you want to deploy and where it should run. I will suggest a clean architecture and a practical deployment plan.
What to prepare
- What you are deploying (model, app, pipeline)
- Target environment (Pi, server, cloud)
- Expected usage volume and latency needs
- Integration needs (web app, dashboard, internal systems)