{"id":953949,"date":"2023-07-05T21:46:30","date_gmt":"2023-07-06T04:46:30","guid":{"rendered":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/?post_type=msr-research-item&p=953949"},"modified":"2023-07-05T21:46:30","modified_gmt":"2023-07-06T04:46:30","slug":"demonstration-of-geyser-provenance-extraction-and-applications-over-data-science-scripts","status":"publish","type":"msr-research-item","link":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/publication\/demonstration-of-geyser-provenance-extraction-and-applications-over-data-science-scripts\/","title":{"rendered":"Demonstration of Geyser: Provenance Extraction and Applications over Data Science Scripts"},"content":{"rendered":"
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As enterprises have started developing and deploying complicated data science workloads at scale, the need for mechanisms that enable enterprise-grade data science (e.g., compliance or auditing) has become more pronounced. In this paper, we present Geyser, an extensible provenance system for data science workloads that can be used as a foundation for enterprise-grade data science. Our system supports both static and dynamic provenance, over a wide range of data science scripts, driven by a knowledge base of data science APIs. We demonstrate the wide applicability of the system using various industrial applications: provenance extraction, model compliance, model linting, model versioning, and poisoning detection. A video of the demonstration is available at https:\/\/aka.ms\/geyserdemo.<\/p>\n<\/div>\n<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

As enterprises have started developing and deploying complicated data science workloads at scale, the need for mechanisms that enable enterprise-grade data science (e.g., compliance or auditing) has become more pronounced. In this paper, we present Geyser, an extensible provenance system for data science workloads that can be used as a foundation for enterprise-grade data science. 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