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    Evaluating Ray: Distributed Python for Massive Scalability

    Dean Wampler provides a distilled overview of Ray, an open source system for scaling Python systems from single machines to large clusters. If you are interested in...

    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...

    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,...

    Model Interpretability with TCAV (Testing with Concept Activation Vectors)

    This Domino Data Science Field Note provides very distilled insights and excerpts from Been Kim’s recent MLConf 2018 talk and research about Testing...

    Creating Multi-language Pipelines with Apache Spark or Avoid Having to Rewrite spaCy into Java

    In this guest post, Holden Karau, Apache Spark Committer, provides insights on how to create multi-language pipelines with Apache Spark and avoid...

    Themes and Conferences per Pacoid, Episode 4

    Paco Nathan's latest column covers themes that include data privacy, machine ethics, and yes, Don Quixote. 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...

    Bias: Breaking the Chain that Holds Us Back

    Speaker Bio: Dr. Vivienne Ming was named one of 10 Women to Watch in Tech by Inc. Magazine, she is a theoretical neuroscientist, entrepreneur, and...

    Reproducible Machine Learning with Jupyter and Quilt

    In this guest blog post, Aneesh Karve, Co-founder and CTO of Quilt, demonstrates how Quilt works in conjunction with Domino's Reproducibility Engine...