Ml-Governance

govML ships 57 templates, 10 profiles, and 27 code generators — battle-tested across 10 frontier projects. It’s the governance framework I built after discovering that every ML project I ran needed the same 30+ documents, and every team was reinventing them from scratch. Why this matters ML governance is the tax you pay to make AI systems auditable, reproducible, and trustworthy. Most teams either skip it (and pay later in failed audits and unreproducible results) or spend weeks building bespoke governance documents for each project. Neither approach scales. govML makes governance a 15-minute setup step instead of a multi-week overhead, by encoding best practices into templates that generate project-specific documentation from configuration. ...

Note (2026-03-20): govML is now maintained as internal tooling. This tutorial references a public repository that is no longer available. For the current description of govML’s methodology, see How I Govern AI-Assisted ML Projects. Problem Statement You run an ML experiment. You tweak a hyperparameter. Three weeks later you cannot reproduce the original result because you forgot which config, which seed, which preprocessing step you changed. You have no contracts, no phase gates, no leakage tests. Your experiment notebook is a graveyard of dead cells and commented-out code. ...

After four ML projects at Georgia Tech, I’d run 14 manual audit cycles with 30+ findings each. The governance wasn’t the problem — the manual enforcement was. So I built govML. The Problem Every ML project needs governance: reproducible experiments, documented decisions, data integrity checks, fair comparisons. But enforcing governance manually is a workflow killer. My unsupervised learning project had 7 audit cycles with 49+ findings. The RL project had 14 cycles with 30+ findings. I was spending more time auditing than experimenting. ...