Subject archive for "model-deployment," page 2

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?” with Amy Heineike, Paco Nathan, and Pete Warden at Domino HQ. Topics discussed include the current state of collaboration around building and deploying models, tension points that potentially arise, as well as practical advice on how to address these 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?” with Amy Heineike, Paco Nathan, and Pete Warden at Domino HQ. Topics discussed include the current state of collaboration around building and deploying models, tension points that potentially arise, as well as practical advice on how to address these tension points.

This blog post includes candid insights about addressing tension points that arise when people collaborate on developing and deploying models. Domino’s Head of Content sat down with Don Miner and Marshall Presser to discuss the state of collaboration between data science and data engineering. The blog post provides distilled insights, audio clips, excerpted quotes as well as the full audio and written transcript. Additional content on this topic will be forthcoming from additional industry experts.

What would you rather be doing? Data science or DevOps?

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

An interesting topic we often hear data science organizations talk about is “unit testing.” It’s a longstanding best practice for building software, but it’s not quite clear what it really means for quantitative research work — let alone how to implement such a practice. This post describes our view on this topic, and how we’ve designed Domino to facilitate what we see as relevant best practices.