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    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, consistency and...

    Understanding Causal Inference

    This article covers causal relationships and includes a chapter excerpt from the book Machine Learning in Production: Developing and Optimizing Data...

    Themes and Conferences per Pacoid, Episode 11

    Paco Nathan's latest article covers program synthesis, AutoPandas, model-driven data queries, and more. Introduction Welcome back to our monthly...

    Feature Engineering: A Framework and Techniques 

    This Domino Field Note provides highlights and excerpted slides from Amanda Casari’s “Feature Engineering for Machine Learning” talk at QCon Sao...

    Model Evaluation

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

    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...

    Managing Data Science as a Capability

    Nick Elprin, CEO at Domino, presented a 3-hour training workshop, “Managing Data Science in the Enterprise”, that provided practical insights and...