Subject archive for "reproducibility," page 2

Data Science

The Machine Learning Reproducibility Crisis

Are We Back in the Dark Ages? Without Source Control?

By Pete Warden9 min read

Data Science

Managing Data Science as a Capability

Nick Elprin, CEO at Domino, presented a 3-hour training workshop, “Managing Data Science in the Enterprise”, that provided practical insights and interactive breakouts. The learnings, anecdotes, and best practices shared in the workshop were based upon years of candid discussions with customers about managing and accelerating data science work. The workshop also featured reusable templates that included a pre-flight data science project checklist as well as a planning template for hiring and onboarding data scientists. We are sharing the breakout materials based on attendee feedback. If you missed Strata and are interested in joining similar discussions, then consider attending Rev.

By Domino5 min read

Data Science

0.05 is an arbitrary cut off: "Turning fails into wins”

Grace Tang, Data Scientist at Uber, presented insights, common pitfalls, and “best practices to ensure all experiments are useful” in her Strata Singapore session, “Turning Fails into Wins”. Tang holds a Ph.D. in Neuroscience from Stanford University.

By Domino5 min read

Data Science

Reproducible Dashboards and Other Great Things to do with Jupyter

Mac Rogers, Research Engineer at Domino, presented best practices for creating Jupyter dashboards at a recent Domino Data Science Pop-Up.

By Domino26 min read

Data Science

Domino for Good: Collaboration, Reproducibility, and Openness, in the Service of Societal Benefit

When I joined Domino Data Lab to lead the Domino for Good initiative a few months ago, it felt like the perfect next step on a path I have been on for a long time.

By Lisa Green6 min read

Product Updates

Git Integration in Domino

We recently released new functionality that provides first-class integration between Domino and git. This post describes the new feature, and describes our perspective on the unique requirements of version control in the context of data science—as distinct from software engineering—workflows.

By Eduardo Ariño de la Rubia5 min read

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