Data Science Blog | Model Deployment

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How To Deploy and Monitor Machine Learning Models

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...

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...

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Addressing Irreproducibility in the Wild

This Domino Data Science Field Note provides highlights and excerpted slides from Chloe Mawer’s "The Ingredients of a Reproducible Machine Learning...

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Data Science vs Engineering: Tension Points

This blog post provides highlights and a full written transcript from the panel, “Data Science Versus Engineering: Does It Really Have To Be This Way?

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Collaboration Between Data Science and Data Engineering: True or False?

This blog post includes candid insights about addressing tension points that arise when people collaborate on developing and deploying models....

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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.,...

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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 ...

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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...

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“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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