====== Cloud AI Training Plan ======

| Phase | Audience | Topics | Courses | Duration |
| Phase 1: Core AI/ML Specialization | All team members | ML Fundamentals, ML in IT Ops, Responsible AI | Google ML Crash Course, AI for Everyone, AWS ML Essentials | Weeks 1–4 |
| Phase 2: MLOps, Automation & Monitoring | ML Engineers, SREs, DevOps | CI/CD for ML, Model Monitoring, Auto-remediation | Coursera MLOps, AWS MLOps Workshop, Azure ML MLOps | Weeks 5–8 |
| Phase 3: Applied Use Cases | All team members | NLP for tickets, Forecasting, Log Anomalies, Generative AI | Fast.ai, DataCamp NLP, Prompt Engineering Guide | Weeks 9–12 |
| Phase 4: Governance, Security, and FinOps | Solution Architects, PMs, Security Engineers | Responsible AI, FinOps, Multi-cloud Governance | AI Governance (Coursera), FinOps Practitioner, Microsoft RA Guide | Weeks 13–14 |
| Capstone Project | Full team | Real-world AI solution with CI/CD, monitoring, dashboards | Internal Project | Weeks 15–18 |

[[wiki:ai:home-page|AI Cloud Managed Services Policies and Procedures]]