The quality of the data in a dataset can have a substantial impact on the performance of a machine learning model that is trained and/or evaluated using the dataset. Effective dataset management, including tasks such as data cleanup, versioning, access control, dataset transformation, automation, integrity and security, etc., can help improve the efficiency and speed of the machine learning process. Currently, engineers spend a substantial amount of manual effort and time to manage dataset versions or to prepare datasets for machine learning tasks. This disclosure describes a platform to manage and use datasets effectively. The techniques integrate dataset management and dataset transformation mechanisms. A storage engine is described that acts as a source of truth for all data and handles versioning, access control etc. The dataset transformation mechanism is a key part to generate a dataset (snapshot) to serve different purposes. The described techniques can support different workflows, pipelines, or data orchestration needs, e.g., for training and/or evaluation of machine learning models.

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Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.