Subject archive for "machine-learning," page 10

This article covers how to detect data drift for models that ingest image data as their input in order to prevent their silent degradation in production.

This Domino Data Science Field Note covers a proposed definition of interpretability and distilled overview of the PDR framework. Insights are drawn from Bin Yu, W. James Murdoch, Chandan Singh, Karl Kumber, and Reza Abbasi-Asi's recent paper, "Definitions, methods, and applications in interpretable machine learning".

This article provides insight on the mindset, approach, and tools to consider when solving a real-world ML problem. It covers questions to consider as well as collecting, prepping and plotting data.

This article covers a couple of key Machine Learning (ML) vital signs to consider when tracking ML models in production to ensure model reliability, consistency and performance in the future. Many thanks to Don Miner for collaborating with Domino on this article. For additional vital signs and insight beyond what is provided in this article, attend the webinar.

Paco Nathan's latest article covers data practices from the National Oceanic and Atmospheric Administration (NOAA) Environment Data Management (EDM) workshop as well as updates from the AI Conference.