Subject archive for "model-context"
By David Weedmark11 min read
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.
By Ann Spencer7 min read
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.
By Domino40 min read
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.
By Andrea Lowe37 min read
This Domino Data Science Field Note covers a proposed definition of machine learning interpretability, why interpretability matters, and the arguments for considering a rigorous evaluation of interpretability. Insights are drawn from Finale Doshi-Velez’s talk, “A Roadmap for the Rigorous Science of Interpretability” as well as the paper, “Towards a Rigorous Science of Interpretable Machine Learning”. The paper was co-authored by Finale Doshi-Velez and Been Kim. Finale Doshi-Velez is an assistant professor of computer science at Harvard Paulson School of Engineering and Been Kim is a research scientist at Google Brain.
By Ann Spencer8 min read
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