Design a centralized model registry for a large enterprise
Tests ML artifact governance at scale. Strong answers cover immutable versioned artifacts with dependency manifests, a framework-agnostic API, and pluggable deployment targets. Red flag: treating models as opaque files without environment reproducibility.
Tests whether a senior engineer can design a multi-tenant model registry that balances flexibility with governance. A strong answer outlines immutable artifact storage with semantic versioning, dependency manifests and container images for reproducibility, a framework-agnostic REST or gRPC API with strong pagination and RBAC, and pluggable deployment targets via abstraction layers. Red flag: proposing a simple file store without lineage tracking, environment isolation, or schema evolution for model metadata.
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- #mlops
- #system-design
- #model-registry
- #machine-learning
- #infrastructure
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