What is Model Inventory?

Success metrics include system uptime, model performance stability, deployment velocity, and operational cost efficiency.

Track model name, version, owner, purpose, deployment status, environment, data sources, performance metrics, risk classification, last evaluation date, dependencies, and compliance status. Include both production models and models in development or staging. Link to model cards for detailed documentation. Track resource consumption and cost per model. For regulated industries, include risk assessment results and approval history. The inventory should be the single source of truth for 'what models do we have running.'

Automate inventory updates through CI/CD pipeline integration so deployments and retirements are captured automatically. Schedule quarterly reviews where model owners verify their entries. Flag models with stale metadata or missing mandatory fields. Use automated discovery scripts that scan deployment platforms for unregistered models. Assign a model inventory owner who is responsible for completeness. Integrate with your deployment platform so the inventory reflects actual production state rather than intended state.

The EU AI Act requires organizations to maintain registries of high-risk AI systems. MAS in Singapore expects financial institutions to inventory all models used in customer decisions. HKMA has similar expectations. Regulators want to know what models are running, what decisions they affect, and who is responsible. Without an inventory, compliance audits become expensive manual investigations. Organizations with up-to-date inventories complete regulatory assessments in days rather than weeks.

Track model name, version, owner, purpose, deployment status, environment, data sources, performance metrics, risk classification, last evaluation date, dependencies, and compliance status. Include both production models and models in development or staging. Link to model cards for detailed documentation. Track resource consumption and cost per model. For regulated industries, include risk assessment results and approval history. The inventory should be the single source of truth for 'what models do we have running.'

Automate inventory updates through CI/CD pipeline integration so deployments and retirements are captured automatically. Schedule quarterly reviews where model owners verify their entries. Flag models with stale metadata or missing mandatory fields. Use automated discovery scripts that scan deployment platforms for unregistered models. Assign a model inventory owner who is responsible for completeness. Integrate with your deployment platform so the inventory reflects actual production state rather than intended state.

The EU AI Act requires organizations to maintain registries of high-risk AI systems. MAS in Singapore expects financial institutions to inventory all models used in customer decisions. HKMA has similar expectations. Regulators want to know what models are running, what decisions they affect, and who is responsible. Without an inventory, compliance audits become expensive manual investigations. Organizations with up-to-date inventories complete regulatory assessments in days rather than weeks.

Track model name, version, owner, purpose, deployment status, environment, data sources, performance metrics, risk classification, last evaluation date, dependencies, and compliance status. Include both production models and models in development or staging. Link to model cards for detailed documentation. Track resource consumption and cost per model. For regulated industries, include risk assessment results and approval history. The inventory should be the single source of truth for 'what models do we have running.'

Automate inventory updates through CI/CD pipeline integration so deployments and retirements are captured automatically. Schedule quarterly reviews where model owners verify their entries. Flag models with stale metadata or missing mandatory fields. Use automated discovery scripts that scan deployment platforms for unregistered models. Assign a model inventory owner who is responsible for completeness. Integrate with your deployment platform so the inventory reflects actual production state rather than intended state.

The EU AI Act requires organizations to maintain registries of high-risk AI systems. MAS in Singapore expects financial institutions to inventory all models used in customer decisions. HKMA has similar expectations. Regulators want to know what models are running, what decisions they affect, and who is responsible. Without an inventory, compliance audits become expensive manual investigations. Organizations with up-to-date inventories complete regulatory assessments in days rather than weeks.