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    The Role of Containers on MLOps and Model Production

    Container technology has changed the way data science gets done. The original container use case for data science focused on what I call, “environment management”....

    Evaluating Generative Adversarial Networks (GANs)

    This article provides concise insights into GANs to help data scientists and researchers assess whether to investigate GANs further. If you are...

    Data Drift Detection for Image Classifiers

    This article covers how to detect data drift for models that ingest image data as their input in order to prevent their silent degradation in...

    Model Interpretability: The Conversation Continues

    This Domino Data Science Field Note covers a proposed definition of interpretability and distilled overview of the PDR framework. Insights are drawn...

    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,...

    Understanding Causal Inference

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

    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...

    Data Ethics: Contesting Truth and Rearranging Power

    This Domino Data Science Field Note covers Chris Wiggins's recent data ethics seminar at Berkeley. The article focuses on 1) proposed frameworks for...

    Seeking Reproducibility within Social Science: Search and Discovery

    Julia Lane, NYU Professor, Economist and cofounder of the Coleridge Initiative, presented “Where’s the Data: A New Approach to Social Science Search...