What is Model Inventory Management?

Implement comprehensive monitoring, automated testing, version control, incident response procedures, and continuous improvement processes aligned with organizational objectives.

Track twelve attributes per model: unique identifier and human-readable name, current lifecycle stage (development, staging, production, deprecated, archived), owning team and primary maintainer contact, deployment location and serving infrastructure details, training data sources with date ranges, key performance metrics with last evaluation date, upstream dependencies (data pipelines, feature stores, other models), downstream consumers (applications, APIs, business processes relying on predictions), compliance classification (risk level, regulatory requirements, last audit date), cost profile (monthly infrastructure spend, cost per prediction), retraining schedule and last retrained date, and known limitations or failure modes. Store this inventory in a searchable database (model registry metadata, Notion, or custom internal tool) rather than spreadsheets that fall out of date.

Implement three governance practices: mandatory model registration (no model reaches production without inventory entry, enforced through deployment pipeline gates), lifecycle review cadence (quarterly review of all production models against usage metrics, performance baselines, and cost thresholds), and retirement criteria (automatically flag models with zero traffic for 30 days, declining performance trending below business thresholds, or dependencies on deprecated data sources). Create a model retirement checklist: notify downstream consumers 30 days before decommissioning, migrate traffic to replacement models, archive artifacts and documentation, and remove infrastructure resources. Track inventory health metrics: total active models, percentage with current documentation, percentage meeting SLO targets, and average model age.

Track twelve attributes per model: unique identifier and human-readable name, current lifecycle stage (development, staging, production, deprecated, archived), owning team and primary maintainer contact, deployment location and serving infrastructure details, training data sources with date ranges, key performance metrics with last evaluation date, upstream dependencies (data pipelines, feature stores, other models), downstream consumers (applications, APIs, business processes relying on predictions), compliance classification (risk level, regulatory requirements, last audit date), cost profile (monthly infrastructure spend, cost per prediction), retraining schedule and last retrained date, and known limitations or failure modes. Store this inventory in a searchable database (model registry metadata, Notion, or custom internal tool) rather than spreadsheets that fall out of date.

Implement three governance practices: mandatory model registration (no model reaches production without inventory entry, enforced through deployment pipeline gates), lifecycle review cadence (quarterly review of all production models against usage metrics, performance baselines, and cost thresholds), and retirement criteria (automatically flag models with zero traffic for 30 days, declining performance trending below business thresholds, or dependencies on deprecated data sources). Create a model retirement checklist: notify downstream consumers 30 days before decommissioning, migrate traffic to replacement models, archive artifacts and documentation, and remove infrastructure resources. Track inventory health metrics: total active models, percentage with current documentation, percentage meeting SLO targets, and average model age.

Track twelve attributes per model: unique identifier and human-readable name, current lifecycle stage (development, staging, production, deprecated, archived), owning team and primary maintainer contact, deployment location and serving infrastructure details, training data sources with date ranges, key performance metrics with last evaluation date, upstream dependencies (data pipelines, feature stores, other models), downstream consumers (applications, APIs, business processes relying on predictions), compliance classification (risk level, regulatory requirements, last audit date), cost profile (monthly infrastructure spend, cost per prediction), retraining schedule and last retrained date, and known limitations or failure modes. Store this inventory in a searchable database (model registry metadata, Notion, or custom internal tool) rather than spreadsheets that fall out of date.

Implement three governance practices: mandatory model registration (no model reaches production without inventory entry, enforced through deployment pipeline gates), lifecycle review cadence (quarterly review of all production models against usage metrics, performance baselines, and cost thresholds), and retirement criteria (automatically flag models with zero traffic for 30 days, declining performance trending below business thresholds, or dependencies on deprecated data sources). Create a model retirement checklist: notify downstream consumers 30 days before decommissioning, migrate traffic to replacement models, archive artifacts and documentation, and remove infrastructure resources. Track inventory health metrics: total active models, percentage with current documentation, percentage meeting SLO targets, and average model age.