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    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 themes and conferences....

    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...
    Code Machine Learning random forest KNN SHAP xgboost Python LIME Models scikit-learn model

    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...
    Data Science Code Model Management Practical Techniques Predictive Models R Item Response Theory Generative Models IRT Models Model Development

    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...
    Data Science Leaders At Work Alignment Model Management Models Featured Data Science and Data Engineering Alignment Practical Techniques Model Production Data Science Leaders Model Myth Model Context IT Leaders Model Governance Models data Model Development

    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...
    Data Science Alignment Model Management Machine Learning Models Featured Data Science and Data Engineering Alignment Practical Techniques Model Context Models Model Development Domino Data Science Field Note

    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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    The Machine Learning Reproducibility Crisis

    Pete Warden is the Technical Lead on the TensorFlow Mobile Embedded Team at Google doing Deep Learning. He is formerly the CTO of Jetpac, which was...
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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. ...
    Data Science Bayesian Methods Model Management Practical Techniques Item Response Theory Models Probabilistic Graphical Models data Model Development model

    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.,...
    Data Science Leaders At Work Alignment CIO Model Technology Practical Techniques Model Production Data Science Leaders Data Analysis Data Scientists Model Deployment IT Leaders Models

    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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