This repository is the production pipeline for *The Little Book of ML Metrics*, an open-source reference book covering 107 ML evaluation metrics across regression, classification, clustering, ranking, computer vision, NLP, GenAI, probabilistic, fairness, and data observability. The codebase is a hybrid of application tooling and book content: it provides a collection of Python scripts (driven by `notebooks/*_figures.py`) that generate publication-quality figures at precise print sizes using matplotlib, a LaTeX compilation toolchain (`book/`) that assembles chapters and figures into a PDF, and several CLI/web utility tools (`tools/formula_tuner.py`, `tools/rebuild_figures.py`, `tools/hardcover.py`) for interactive formula layout, figure manifest management, and PDF preflight verification. A GitHub Actions workflow builds the book nightly. The primary user workflow is figure generation followed by LaTeX compilation, but the repository also includes a local review web app (`tools/review_recorder.py`) for page-by-page annotation. External dependencies include numpy, scikit-learn, opencv-python, yfinance, jupyter, and an optional set of ML model libraries (torch, transformers, lpips, pesq, pystoi, bert-score) used only for GenAI data generation. The LaTeX build requires XeLaTeX. While the repo is not a traditional software application or data pipeline, its tooling components exhibit solid engineering practices and are partially reusable in other book/figure production workflows.