Data Science Blog

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...

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Data Science vs Engineering: Tension Points

This blog post provides highlights and a full written transcript from the panel, “Data Science Versus Engineering: Does It Really Have To Be This Way?

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Trust in LIME: Yes, No, Maybe So?

In this Domino Data Science Field Note, we briefly discuss an algorithm and framework for generating explanations, LIME (Local Interpretable...

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Item Response Theory in R for Survey Analysis

In this guest blog post, Derrick Higgins, of American Family Insurance, covers item response theory (IRT) and how data scientists can apply it within...

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Themes and Conferences per Pacoid, Episode 1

Introduction: New Monthly Series! Welcome to a new monthly series! I’ll summarize highlights from recent industry conferences, new open source...

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Classify All the Things (with Multiple Labels)

Derrick Higgins of American Family Insurance presented a talk, “Classify all the Things (with multiple labels): The most common type of modeling task...

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Model Management and the Era of the Model-Driven Business

Over the past few years, we’ve seen a new community of data science leaders emerge. Regardless of their industry, we have heard three themes emerge...

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Put Models at the Core of Business Processes

At Rev, Nick Elprin, Domino's CEO, continued to provide insights on managing data science based upon years of candid discussions with customers. He...

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Data Science Models Build on Each Other

Alex Leeds, presented “Building Up Local Models of Customers” at a Domino Data Science Popup. Leeds discussed how the Squarespace data science team...

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Data Science is more than Machine Learning

This Domino Data Science Field Note provides highlights and video clips from Addhyan Pandey’s Domino Data Pop-Up talk, “Leveraging Data Science in...

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Using Bayesian Methods to Clean Up Human Labels

Derrick Higgins, AmFam Data Science & Analytics, discusses how Bayesian methods can be applied to improve the quality of annotated training sets. ...

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What Your CIO Needs to Know about Data Science

What would you rather be doing? Data science or DevOps? As a data scientist, your CIO may hear from you that model deployment is a challenge (e.g.,...

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Humans in the Loop

This guest blog post from Paco Nathan dives into how people and machines collaborating together to perform work is real and not science...

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Model Deployment Powered by Kubernetes

In this article we explain how we’re using Kubernetes to enable data scientists to deploy predictive models as production-grade APIs. Background ...

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