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    Machine Learning Modeling: How It Works and Why It’s Important

    Models are the central output of data science, and they have tremendous power to transform companies, industries, and society.  At the center of every machine learning or...

    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,...
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    Understanding Causal Inference

    This article covers causal relationships and includes a chapter excerpt from the book Machine Learning in Production: Developing and Optimizing Data...
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    Towards Predictive Accuracy: Tuning Hyperparameters and Pipelines

    This article provides an excerpt of “Tuning Hyperparameters and Pipelines” from the book, Machine Learning with Python for Everyone by Mark E....
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    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...
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    Make Machine Learning Interpretability More Rigorous

    This Domino Data Science Field Note covers a proposed definition of machine learning interpretability, why interpretability matters, and the...
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    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...
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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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    Model Evaluation

    This Domino Data Science Field Note provides some highlights of Alice Zheng’s report, "Evaluating Machine Learning Models", including evaluation...
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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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    On Ingesting Kate Crawford’s “The Trouble with Bias”

    Kate Crawford discussed bias at a recent SF-based City Arts and Lectures talk and a recording of the discussion will be broadcast, May 6th, on KQED...
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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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    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...
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    0.05 is an Arbitrary Cut Off: "Turning Fails into Wins”

    Grace Tang, Data Scientist at Uber, presented insights, common pitfalls, and “best practices to ensure all experiments are useful” in her Strata...
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    Data Science Use Cases

    In this post, Don Miner covers how to identify, evaluate, prioritize, and pick which data science problems to work on next. Don is a co-founder of ...
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