When implementing an MLOps pipeline, which component is crucial for managing version control and tracking changes in model experiments?

A Model Registry is crucial for managing version control and tracking changes in model experiments

within an MLOps pipeline. It serves as a centralized repository to store, version, and manage trained

models, their metadata (e.g., hyperparameters, performance metrics), and experiment history,

ensuring reproducibility and governance. NVIDIA’s AI Enterprise suite, including tools like NVIDIA

NGC, supports model registries for streamlined MLOps. Option A (CI System) focuses on code

integration, not model tracking. Option C (Orchestration Platform) manages workflows, not

versioning. Option D (Artifact Repository) stores general outputs but lacks model-specific features.

NVIDIA’s MLOps documentation emphasizes the registry’s role in AI lifecycle management.

Reference: NVIDIA AI Enterprise MLOps (www.nvidia.com), NVIDIA NGC Catalog

(catalog.ngc.nvidia.com).