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    A Guide to Machine Learning Model Deployment

    Machine-learning (ML) deployment involves placing a working ML model into an environment where it can do the work it was designed to do. The process of model deployment and...

    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...

    Defining Metrics to Drive Machine Learning Model Adoption & Value

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

    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 ...

    “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,...

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