Model Registry

As enterprise AI programs scale beyond a handful of models, management complexity grows rapidly. Different teams train models on different data, deploy to different serving infrastructure, and make upgrade decisions without visibility into what else might be affected. The model registry solves the "model sprawl" problem by providing a single place where every model — from experimental fine-tunes to production-critical recommendation engines — is registered, versioned, and governed.

A model registry stores more than just model files. For each registered model it captures: the model artifact (weights, configuration, tokenizer); **lineage metadata** (which training dataset, code commit, and experiment run produced it); **evaluation results** (benchmark scores, offline quality metrics, bias assessments); **deployment history** (which serving endpoints have run this version, and when); and **approval status** (which stage in the promotion workflow — staging, shadow, canary, production, retired — the model currently occupies). This metadata transforms model management from institutional memory held in individual engineers' heads into a structured, queryable system.

For the enterprise, the model registry is a governance instrument as much as an operational tool. It answers the questions that model risk management frameworks demand: Can you identify every model currently making decisions in production? Can you reproduce the exact model artifact used on a specific date? Can you immediately identify and roll back a model version in response to an incident? Can you demonstrate that models were reviewed and approved before deployment? Mature organizations integrate their model registry with their CI/CD pipeline, requiring registry promotion as a mandatory step in the model deployment workflow.