We have 3 of 19 papers
We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.
Not the right person? Other researchers publish under this name.
The article is suggesting a framework for integrating XAI into large data pipelines, thus presenting each AI-powered change as easily understandable, and enabling teams to debug quicker, audit more efficiently, and gain trust to a greater extent.
The key parts and overall layout of JupyterOps are described, including a drastic decrease in deployment time, better model reproducibility, and increased cross-functional collaboration between data engineers, scientists, and DevOps teams are outlined.
This document investigates a futuristic programmatic governance way of handling compliance issues by combining Policy-as-Code (PaC) and Machine Learning (ML), indicating faster response time, shrinkage of compliance gaps, and notable cost savings as compared to the traditional governance models.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
