Artificial Intelligence

Curriculum

• AI fundamentals: how a machine sees/learns; neural networks and the probabilistic nature of LLMs
• The four pillars of agentic work: rule · skill · MCP · hook
• Git/GitHub and dashboard anatomy; Design System with Claude and a single-source repo (token, hook, skill)
• The Google Workspace universe and automation with Apps Script
• Connecting corporate memory to the system with NotebookLM and Gemini Gems
• A look ahead with Google Antigravity

Curriculum

• The logic of workflow automation
• Building visual flows: node / trigger / link, serial-parallel flows and error handling
• PLM integration with basic Python coding (Enovia)
• ERP integration with basic Python coding (SAP, IFS)
• Process mapping (7-part skeleton), fit testing and prioritization with an impact/difficulty matrix
• The real ROI of automation (time + consistency + error reduction); goal: a team that runs automation

Curriculum

• The difference between rule-based systems, off-the-shelf AI camera engines, and end-to-end designed vision systems
• Camera, lighting, and lens selection; multi-light, macro/micro, and 3D metrology scenarios
• Anomaly-based modeling: learning defect logic from 'good part' data alone
• Explainable decisions via heat maps (OK / NOK / Conditional)
• Safety applications: PPE detection, restricted-zone violations, ergonomics/posture analysis
• MES/ERP integration and the basics of model monitoring (MLOps)