For many organizations, how a dataset or model is really created remains a mystery. There are many nested levels of transformations and understanding the full picture is no less than wizardry. Layer keeps a comprehensive bookkeeping of how different entities depend on each other.
Without having full visibility of a pipeline and the dependency graph, it's hard to pinpoint problems. Layer gives you the tools to debug quickly.
Layer gives you full provenance of the data entities for audit trails that can significantly facilitate compliance efforts.
Combined with the versioning capabilities of Layer, data scientists and engineers can easily delve into how model discovery experiments evolved.
With the Declarative approach, you always keep the data lineage of a feature, model or even a dataset by tracking not just the dependencies, but also the underlying code.
Centralized location for AI management across the enterprise, as well as a way to identify any duplication of efforts and opportunities for cost savings
Easy way to access performance
and audit logs for compliance purposes
Role-based access controls and better approach for managing AI security
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