The Little Book of ML Metrics is a comprehensive, collaboratively authored reference book that explains and visualizes evaluation metrics across the full spectrum of machine learning tasks: regression, classification, clustering, ranking, computer vision, NLP, generative AI, probabilistic estimation, bias/fairness, and data observability. The repository contains the LaTeX source code, a collection of Jupyter notebooks (and Python scripts) that generate all figures, and a set of custom tools for book production and review. Its primary function is to produce a high-quality, print-ready PDF that serves as both a pedagogical introduction and a practical decision guide for practitioners. The codebase is dominated by figure-generation scripts that implement each metric with synthetic or small real-world datasets and render Tufte-style, publication-quality plots. While the repository is not a traditional software application, it does include several bespoke tools: an interactive formula tuner (for adjusting TikZ arrow annotations on formulas), a review recorder (a local HTTP server for reviewing the PDF with audio notes), and a formula layout helper that auto-positions arrows. These tools are tightly integrated into the book production pipeline but are not intended for general reuse. The code is well-organized and follows consistent visual patterns (shared color palette, despine helpers, direct annotations), though there is considerable duplication across notebook scripts and a lack of automated tests. The LaTeX build process is robust but depends on a full TeX distribution and a third-party GitHub Action. Dependencies are pinned via pyproject.toml and uv.lock, and the genai figure generation requires a separate heavy environment (torch, transformers) run manually, which reduces full reproducibility out-of-the-box. Overall, this is a well-crafted book project with strong internal consistency, but its code is best viewed as supporting infrastructure rather than a standalone piece of software.