The source of truth for everything about every model across the enterprise
Evergreen Model Inventory is the convergence point of model innovation and model productionization.
How many models are in production across the enterprise?
Is the documentation complete? Has the model been validated?
Who is productionizing them?
With an evergreen AI model inventory, you always know.
An always-updated system of record for all production-ready models across the enterprise
Model development requires the freedom to innovate and model productionization requires standardization. The evergreen AI model inventory is the convergence point that brings these contrasting disciplines together to develop a single production model inventory for the enterprise.
How we do it:
The evergreen enterprise model inventory is the system of record for all models, regardless of the type, whether developed in-house or purchased from a vendor, tools used to create the model, or where it runs.
The model inventory is the foundation for establishing a life-time audit trail for all production models and is the first step in the production model life cycle.
Separating data science and ModelOps
Model productionization is the hand-off from data science teams to IT. Without standardized controls and processes, productionized models inherit operational and compliance risk.
How we do it:
Automated orchestration ensures every step is completed for creating and maintaining a standard and persistent representation of each and every production model in the evergreen model inventory.
A standardized production process verifies all steps for a secure pipeline to operationalization are completed and tracked for auditability.
Continuous model operations
Models are constantly making decisions and must be continuously tracked and monitored for accuracy and compliance from inception.
How we do it:
Automated model life cycles establish provenance and a complete model lineage for every model registered in the model inventory.
Integration with an expansive set of model creation tools, IT systems, model risk management and compliance systems ensure auditability and reproducibility of model changes, problems, and performance.
See how ModelOp Center can govern and scale your AI initiatives.
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