AI Systems & Model Deployment
I build machine learning systems that go beyond experiments. Models are packaged into reliable services, deployed in real environments, and integrated with applications that people actually use.
Outcomes
- Models that can be used via API or application
- Consistent and reproducible predictions
- Clear interfaces for integration
- Performance that meets real usage needs
- Systems that can be monitored and maintained
Common use cases
Prediction APIs
Serve models for pricing, forecasting, classification, or scoring.
Automation and decision support
Embed predictions into workflows to reduce manual decisions.
Edge or on-device inference
Run models locally when latency or connectivity matters.
Internal ML services
Centralized models used across dashboards or applications.
How I work
- Clarify the problem and success criteria
- Review data and modeling feasibility
- Train and evaluate models
- Package models into services or APIs
- Deploy and validate in real usage
- Document and hand over
What you get
- Deployed AI service or API
- Model files and versioning strategy
- Clear request and response schemas
- Deployment and usage documentation
- Optional demo UI
Featured Projects
VialVision
An edge-AI computer vision system using YOLO on Raspberry Pi to detect and classify vial conditions.
Volto - Energy Monitoring
Implemented a real-time electricity usage monitoring dashboard with forecasting and anomaly detection.
Used Car Price Prediction
Deployed a machine learning model via REST API to predict car prices based on features.
Ready to deploy AI?
Email me what you want to automate or predict. I will help you design a practical AI system that fits your environment.
What to prepare
- Problem and expected output
- Available data
- Target environment
- Integration needs