Production-Grade AI Engineering Resources

12 print-ready, double-sided reference cards covering every domain tested in ML engineering interviews. 26 pages. Print once, use forever.

180+ terms across ML, Deep Learning, LLMs, MLOps, and Data Engineering — defined precisely for practitioners, not for Wikipedia.

250+ production-ready prompts for software engineers across 8 categories: code review, documentation, debugging, system design, testing, performance, and ML engineering.

25 fully worked case studies with complete 7-step solutions. YouTube recommendation, fraud detection, search ranking, content moderation, LLM evaluation — and 20 more.

End-to-end architectural reference for building production ML/AI systems. 10 architecture patterns, data pipeline design, training at scale, serving patterns, and 20 production checklists.

15 blueprints for adding AI features to any product: semantic search, RAG assistants, classification, churn prediction, personalization, fraud detection.

Complete practitioner guide for building LLM-powered applications. Model selection, prompt engineering patterns, RAG architecture, fine-tuning with LoRA, evaluation, and production cost/latency optimization.

Everything for your interview campaign: 90-day Notion calendar, resume templates (LaTeX + DOCX), mock interview scorecards, and company-specific deep-dive checklists.

For engineering teams adding AI to their products. Includes team AI readiness assessment, project kickoff Notion template, role-specific learning tracks, and a 90-day team roadmap.

6-module self-paced course: LLM foundations, RAG systems, fine-tuning, evaluation, production serving, and capstone project. Includes GitHub repo access and completion certificate.