Subject archive for "reproducibility," page 3

Data science is no longer a specialization of a single person or small group. It is now a key source of competitive advantage, and as a result, the scale of projects continues to grow. Collaboration is critical because it enables teams to take on larger problems than any individual. It also allows for specialization and a shared context that reduces dependency on "unicorn" employees who don't scale and are a major source of key-man risk. The problem is that collaboration is a vague term that blurs multiple concepts and best practices. In this post, we clarify the differences between repeatability, reproducibility, and whenever possible the golden standard of replicability. By establishing best practices of frictionless in-team and cross-team collaboration, you can dramatically improve the efficiency and impact of your data science efforts.

Managing “environments” (i.e., the set of packages, configuration, etc.) is a critical capability of any Data Science Platform. Not only does environment setup waste time on-boarding people, but configuration issues across environments can undermine reproducibility and collaboration, and can introduce delays when moving models from development to production.

Today we’re announcing that we have raised $10.5 million in a funding round led by Sequoia Capital.

This guest post was written by Arnu Pretorius, a Masters student in Mathematical Statistics at the MIH Media Lab, Stellenbosch University. Arnu's research interests include machine learning and statistical learning theory.

At last week's useR! R User conference, I spoke on digital provenance, the importance of reproducible research, and how Domino has solved many of the challenges faced by data scientists when attempting this best practice. More on the topic, and a recording of the talk, below.

Every year, $50 of your taxes is invested in research that can't be reproduced.
Erik Andrejko, VP Science, The Climate Corporation, speaking at Strata+Hadoop World San Jose 2016