Subject archive for "model-development," page 2

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 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.

Paco Nathan's latest article covers program synthesis, AutoPandas, model-driven data queries, and more.

Pete Skomoroch presented “Product Management for AI” at Rev. This post provides a distilled summary, video, and full transcript.

Our last release, Domino 3.3 saw the addition of two major capabilities: Datasets and Experiment Manager. “Datasets”, a high-performance, revisioned data store offers data scientists the flexibility they need to make use of large data resources when developing models. And “Experiment Manager” acts as a data scientist’s “modern lab notebook” for tracking, organizing, and finding everything tested over the course of their research.