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A data team is developing machine learning models in Google Cloud. To satisfy strict governance and regulatory compliance standards, the team must ensure complete reproducibility and auditability. They need a centralized approach to track model versions, record runtime parameters and evaluation metrics, and capture the end-to-end provenance linking training datasets, execution steps, and resulting model assets.
Which Google Cloud approach best satisfies these model metadata management and governance requirements?
Manage model versions and deployment in the Model Registry, while recording pipeline steps, workflow artifacts, and artifact lineage using Vertex ML Metadata.
Deploy models directly to serving endpoints and use Cloud Logging query filters as the primary system of record for model documentation.
Store serialized model weights in Cloud Storage buckets and rely on custom object metadata headers to document training runs and lineage.
Query BigQuery INFORMATION_SCHEMA views to automatically inspect model schemas, training parameters, and lineage relationships across projects.
Manage model versions and deployment in the Model Registry, while recording pipeline steps, workflow artifacts, and artifact lineage using Vertex ML Metadata.
The Model Registry is a centralized repository in Google Cloud designed to oversee the lifecycle of machine learning models by organizing versions, evaluating quality, and managing deployments to serving endpoints. Complementing this, Vertex ML Metadata provides a purpose-built metadata management service that captures, organizes, and tracks the execution steps, inputs, and outputs across the machine learning lifecycle using a graph-based data model.
Combining the Model Registry with Vertex ML Metadata is Google Cloud's purpose-built architectural pattern for ML governance. It eliminates manual record-keeping, ensures deterministic reproducibility, and delivers the rigorous provenance required for compliance.
Deploy models directly to serving endpoints and use Cloud Logging query filters as the primary system of record for model documentation.
Store serialized model weights in Cloud Storage buckets and rely on custom object metadata headers to document training runs and lineage.
Query BigQuery INFORMATION_SCHEMA views to automatically inspect model schemas, training parameters, and lineage relationships across projects.
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