Skip to content
    Latest

    Defining clear metrics to drive model adoption and value creation

    One of the biggest ironies of enterprise data science is that although data science teams are masters at using probabilistic models and diagnostic analytics to forecast...

    What Your CIO Needs to Know about Data Science

    What would you rather be doing? Data science or DevOps? As a data scientist, your CIO may hear from you that model deployment is a challenge (e.g.,...

    Model Deployment Powered by Kubernetes

    In this article we explain how we’re using Kubernetes to enable data scientists to deploy predictive models as production-grade APIs. Background ...

    Building and Delivering Risk Models to Global Insurance Companies

    We’re excited to share our latest customer case study, about how KatRisk, a leading catastrophe risk modeling firm, used Domino to deploy its models...

    “Unit testing” for data science

    An interesting topic we often hear data science organizations talk about is “unit testing.” It’s a longstanding best practice for building software,...