Which type of MLOps tooling specifically addresses the need for managing and versioning data used in ML workflows?

The correct answer is the Dataset Registry. This type of tooling is specifically designed to manage and version the datasets used in machine learning workflows. In machine learning operations (MLOps), managing data is critical as it directly influences the training and performance of models. A Dataset Registry provides capabilities to track various versions of datasets, ensuring reproducibility and traceability in ML experiments.

Having a well-maintained Dataset Registry allows data scientists and ML engineers to easily access different versions of the datasets they need, manage their lineage, and confirm that the data used for training, validation, and testing is documented and appropriately versioned. This is particularly important for compliance and collaboration among different teams or projects, where understanding the history and changes in datasets can significantly affect the outcomes of various ML applications.

While Feature Stores are important for managing and serving features used in models, and Model Repositories are focused on storing and versioning models themselves, the primary focus on managing and versioning datasets specifically falls under the realm of a Dataset Registry. Pipeline Orchestration Tools assist with automating and managing ML workflows but do not directly address the need to handle the data itself.