Gen AI data chain at scale
Generative AI workflows heavily rely on data-centric tasks—such as filtering samples by annotation fields, vector distances, or scores produced by custom classifiers. At the same time, computer vision datasets are quickly approaching petabyte volumes, rendering data wrangling difficult. In addition, the iterative nature of data preparation necessitates robust dataset
Generative AI workflows heavily rely on data-centric tasks—such as filtering samples by annotation fields, vector distances, or scores produced by custom classifiers. At the same time, computer vision datasets are quickly approaching petabyte volumes, rendering data wrangling difficult. In addition, the iterative nature of data preparation necessitates robust dataset sharing and versioning mechanisms, both of which are hard to implement ad-hoc. In this workshop we will introduce DVCx - an upcoming product by Iterative that separates the storage and processing of samples from metadata and enables data-centric operations at scale for machine learning teams and individual researchers.
Speaker:
Tibor Mach is a Machine Learning Solutions Engineer at Iterative.ai. He has been working in ML and MLOps in the past 5 years. Tibor has a Ph.D in mathematics from the University of Göttingen and had published papers in the field of probability theory prior to refocusing to ML.
