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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”....

    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...

    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...

    Product Management for AI

    Pete Skomoroch presented “Product Management for AI” at Rev. This post provides a distilled summary, video, and full transcript. Session Summary Pete...

    Announcing Domino 3.4: Furthering Collaboration with Activity Feed

    Our last release, Domino 3.3 saw the addition of two major capabilities: Datasets and Experiment Manager. “Datasets”, a high-performance, revisioned...

    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...

    Manipulating Data with dplyr

    Special thanks to Addison-Wesley Professional for permission to excerpt the following "Manipulating data with dplyr" chapter from the book, ...

    Announcing Domino 3.3: Datasets and Experiment Manager

    Our mission at Domino is to enable organizations to put models at the heart of their business. Models are so different from software — e.g., they...

    Themes and Conferences per Pacoid, Episode 7

    Paco Nathan covers recent research on data infrastructure as well as adoption of machine learning and AI in the enterprise. Introduction Welcome back...

    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...