Data & AI guides.

The machine learning concepts every cloud AI exam shares — data prep, feature engineering, model selection, evaluation metrics and MLOps — with zeros in math anxiety.

Batch vs. streaming, orchestration, data quality gates and medallion architectures — the recurring patterns behind DEA-C01, DP-203 and PDE.

Structured prompting, retrieval patterns, guardrails and evaluation — a serious practitioner's guide to getting reliable output from LLMs at work.

Embeddings, similarity search, chunking strategy and index tuning — everything you need before your first production semantic search feature.