Each feature maps directly to a professional certification — this is applied knowledge, not just coursework.
| Certification | Hours | Feature Demonstrated | Code Location |
|---|---|---|---|
| IBM Generative AI Engineering (PyTorch, LangChain, HuggingFace) | 144h | Multi-strategy Claude orchestration with tool_use; structured output Pydantic schemas; dynamic model routing |
ghl_real_estate_ai/services/claude_orchestrator.py |
| IBM RAG and Agentic AI | 24h | BM25 + dense vector hybrid retrieval with RRF fusion; pgvector HNSW indexing; semantic cache | advanced_rag_system/ |
| DeepLearning.AI Deep Learning Specialization | 120h | ChromaDB embedding store; dense vector similarity search; 768-dim embeddings pipeline | advanced_rag_system/src/embeddings/ |
| Duke LLMOps Specialization | 48h | 3-tier Redis cache (L1 memory/L2 Redis/L3 pgvector) with 87% hit rate; per-model cost optimization; model A/B testing | ghl_real_estate_ai/services/claude_orchestrator.py, ghl_real_estate_ai/services/jorge/ab_testing_service.py |
| Microsoft AI & ML Engineering | 75h | Agent mesh coordinator for multi-bot orchestration; SLA tracking; performance-based routing; circuit breaker patterns | ghl_real_estate_ai/services/agent_mesh_coordinator.py, ghl_real_estate_ai/services/performance_monitor.py |
| Vanderbilt Generative AI Strategic Leader | 40h | Multi-LLM orchestration strategy (Claude + Gemini + Perplexity); autonomous agent coordination; cost governance; task-complexity-based model routing | ghl_real_estate_ai/services/claude_orchestrator.py, ghl_real_estate_ai/core/llm_client.py |
| Google Cloud GenAI Leader | 25h | Docker Compose multi-service deployment; Render Blueprint IaC; production-ready containerization | docker-compose.yml, render.yaml |
| IBM BI Analyst | 141h | Multi-page Streamlit BI dashboard; executive KPI reporting; lead intelligence visualizations | ghl_real_estate_ai/streamlit_demo/ |
| Google Data Analytics | 181h | P50/P95/P99 latency tracking; per-model performance analytics; error monitoring dashboards | ghl_real_estate_ai/services/performance_monitor.py, ghl_real_estate_ai/services/jorge_performance_monitor.py |
| Google Advanced Data Analytics | 200h | Advanced ML lead scoring engine; SHAP-informed feature importance; ensemble scoring | ghl_real_estate_ai/services/advanced_ml_lead_scoring_engine.py |
| Microsoft Data Visualization | 87h | Plotly charts; LLM cost dashboard; cache performance panels; Prometheus + Grafana 9-panel observability | ghl_real_estate_ai/streamlit_demo/ |
| Google Digital Marketing & E-commerce | 190h | Multi-touch attribution modeling (first-touch, last-touch, linear, time-decay, position-based); marketing automation MCP server (email, SMS, social campaigns); customer journey tracking API; competitive intelligence | ghl_real_estate_ai/analytics/revenue_attribution_engine.py, mcp_servers/marketing_automation_mcp.py |
| Python for Everybody (U. Michigan) | 60h | Python foundations across entire codebase: FastAPI API, SQLAlchemy + Alembic models, aiohttp async clients, dataclasses, type hints, PostgreSQL + Redis integration | portal_api/app.py, ghl_real_estate_ai/database/, alembic/ |
| Linux Foundation OSS Development | 60h | GitHub Actions CI (lint, test, coverage, security scan, Docker build); dependabot; Makefile automation; multi-stage Dockerfiles; Docker Compose multi-service orchestration; bash setup scripts; CONTRIBUTING.md + CODE_OF_CONDUCT | .github/workflows/, Makefile, docker-compose.yml, setup.sh |
| Anthropic Claude Code in Action | 3h | Claude Code hooks (PreToolUse security, PostToolUse, caching enforcer); 20+ Claude Code agent definitions; MCP tool adapter and server exporter for AgentForge; GHL CRM MCP server; .claude/CLAUDE.md project config |
.claude/hooks/, .claude/agents/, agentforge/agentforge/tools/mcp.py, mcp-servers/ghl_crm_server.py |
Total verified training hours applied: 1,398h across 15 certifications from IBM, DeepLearning.AI, Duke, Vanderbilt, Google, Microsoft, U. Michigan, Linux Foundation, and Anthropic.
Unmapped certifications (display-only): 6 certifications have no direct code mapping in this repository -- Google BI, Vanderbilt ChatGPT Personal Automation, DeepLearning.AI AI For Everyone, Microsoft GenAI for Data Analysis, Microsoft AI-Enhanced Data Analysis, and Meta Social Media Marketing. These are either executive/strategic or domain-specific credentials that don't correspond to implemented features here.