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    Trending Toward Concept Building - A Review of Model Interpretability for Deep Neural Networks

    We are at an interesting time in our industry when it comes to validating models - a crossroads of sorts when you think about it. There is an opportunity for practitioners...

    On Being Model-driven: Metrics and Monitoring

    This article covers a couple of key Machine Learning (ML) vital signs to consider when tracking ML models in production to ensure model reliability,...

    Time Series with R

    This article delves into methods for analyzing multivariate and univariate time series data. A complementary Domino project is available. Introduction

    Manual Feature Engineering

    Many thanks to AWP Pearson for the permission to excerpt "Manual Feature Engineering: Manipulating Data for Fun and Profit" from the book, Machine...

    Themes and Conferences per Pacoid, Episode 9

    Paco Nathan's latest article features several emerging threads adjacent to model interpretability. Introduction Welcome back to our monthly burst of...

    SHAP and LIME Python Libraries: Part 2 - Using SHAP and LIME

    This blog post provides insights on how to use the SHAP and LIME Python libraries in practice and how to interpret their output, helping readers...

    The Past/Present/Future + Myths of Data Science

    Sivan Aldor-Noiman, VP of Data Science at Wellio, presented “The Past/Present/Future + Myths of Data Science” at Domino. This blog post provides a...

    Model Evaluation

    This Domino Data Science Field Note provides some highlights of Alice Zheng’s report, "Evaluating Machine Learning Models", including evaluation...

    Domino Honored to Be Named Visionary in Gartner Magic Quadrant

    The team at Domino is proud to be named a Visionary for the second year in a row in Gartner’s Magic Quadrant for Data Science and Machine Learning...