Subject archive for "engineering," page 4

In this Domino Data Science Field Note, we briefly discuss an algorithm and framework for generating explanations, LIME (Local Interpretable Model-Agnostic Explanations), that may help data scientists, machine learning researchers, and engineers decide whether to trust the predictions of any classifier in any model, including seemingly “black box” models.

Introduction: New Monthly Series!

This Domino Field Note provides highlights and excerpted slides from Amanda Casari’s “Feature Engineering for Machine Learning” talk at QCon Sao Paulo. Casari is the Principal Product Manager + Data Scientist at Concur Labs. Casari is also the co-author of the book, Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists. The full video of the talk is available here and special thanks to Amanda for providing permission to Domino to excerpt the talk’s slides in this Domino Field Note.

Are We Back in the Dark Ages? Without Source Control?

Aneesh Karve, Co-founder and CTO of Quilt, visited the Domino MeetUp to discuss the evolution of data infrastructure. This blog post provides a session summary, video, and transcript of the presentation. Karve is also the author of "Reproducible Machine Learning with Jupyter and Quilt".

Reproducible machine learning with Jupyter and Quilt