Capstone Blueprints (Industry-Ready, GDPR-Safe)

These capstones are portfolio-grade project blueprints designed to make you company job-ready (ML Engineer / LLM Engineer / Data & Analytics). They are written to be safe for a public GitHub repo:

• Use public datasets (download yourself) or synthetic data
• Do not commit datasets, model artifacts, logs, or credentials (this repo’s .gitignore already enforces that)
• Avoid any personally identifiable information (PII) or private company documents

Note: This is educational content, not legal advice. See DISCLAIMER.md for data/privacy context.

Capstones

1) ML Engineer Capstone: Real-Time Risk Scoring System

• Blueprint: capstone-ml-engineer.md
• Focus: end-to-end pipeline, API serving, monitoring, retraining, cost/reliability thinking

2) LLM Engineer Capstone: RAG Knowledge Assistant (with evaluation + guardrails)

• Blueprint: capstone-llm-rag-engineer.md
• Focus: ingestion → embeddings → retrieval → generation, eval harness, prompt-injection defenses

3) Data/Analytics Capstone: SQL → Metrics → Dashboard → ML

• Blueprint: capstone-data-analytics-sql-ml.md
• Focus: analytics engineering, SQL case studies, business metrics, and a productionized ML model

Public Repo GDPR-Safe Checklist (quick)

• No PII: no names, emails, addresses, phone numbers, IPs, exact GPS, device identifiers
• No secrets: .env stays local; never commit API keys/tokens
• No datasets committed: only provide download links or synthetic generators
• No raw logs: logs can contain identifiers; keep them local
• Document tradeoffs: explain privacy decisions and data minimization in your README