Industrial AI | Data & Integration Architectures | KPI & Decision Logic | Green AI

I use GitHub as a small, curated portfolio of practical artifacts that connect my LinkedIn positioning with selected technical implementations.

The focus is not on collecting badges or rebuilding tutorials. The focus is on translating selected learning, research and positioning themes into reproducible working artifacts.

My work is centered on the intersection of:

• Industrial AI
• data and integration architectures
• KPI and decision logic
• cloud and data platform concepts
• Green AI / Sustainable AI as a cost, energy, reporting and governance topic

GitHub is used here as supporting evidence: small repositories, visible outputs, clear assumptions and explicit limitations.

• LinkedIn
• ORCID

Synthetic demo translating accuracy, runtime, energy, CO₂ and cost metrics into a simple Industrial AI trade-off and decision logic.

This repository shows:

• KPI-based model comparison
• energy, CO₂, runtime and cost trade-offs
• simple decision scoring
• Pareto-style comparison logic
• explicit synthetic-data limitations

Repository: industrial-green-ai-kpi-demo

Small decision-support demo for prioritizing Industrial AI / Data use cases based on KPI pressure, data readiness, integration effort, sponsor fit and operational feasibility.

This repository shows:

• use-case scoring
• ranking logic
• risk and feasibility separation
• sensitivity analysis
• explicit assumptions and limitations

Repository: industrial-use-case-prioritization-scorecard

Each repository should answer four questions quickly:

1. What problem does it address?
2. What is implemented?
3. What output is produced?
4. What are the limits?

I prefer a few clean repositories with visible outputs over many disconnected exercises.