Subject archive for "model-management," page 3

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 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.

This article covers causal relationships and includes a chapter excerpt from the book Machine Learning in Production: Developing and Optimizing Data Science Workflows and Applications by Andrew Kelleher and Adam Kelleher.

This article provides an excerpt of “Tuning Hyperparameters and Pipelines” from the book, Machine Learning with Python for Everyone by Mark E. Fenner. The excerpt evaluates hyperparameters including GridSearch and RandomizedSearch as well as building an automated ML workflow.

Many thanks to AWP Pearson for the permission to excerpt "Manual Feature Engineering: Manipulating Data for Fun and Profit" from the book, Machine Learning with Python for Everyone by Mark E. Fenner.

Julia Lane, NYU Professor, Economist and cofounder of the Coleridge Initiative, presented “Where’s the Data: A New Approach to Social Science Search & Discovery” at Rev. Lane described the approach that the Coleridge Initiative is taking to address the science reproducibility challenge. The approach is to provide remote access for government analysts and researchers to confidential data in a secure data facility and to build analytical capacity and collaborations through an Applied Data Analytics training program. This article provides a distilled summary and a written transcript of Lane’s talk at Rev. Many thanks to Julia Lane for providing feedback on this post prior to publication.