Tag: Reproducibility

Learn from the Reproducibility Crisis in Science

Key highlights from Clare Gollnick’s talk, “The limits of inference: what data scientists can learn from the reproducibility crisis in science”, are covered in this Domino...

Data Scientist? Programmer? Are They Mutually Exclusive?

This Domino Data Science Field Note blog post provides highlights of Hadley Wickham’s ACM Chicago talk, “You Can’t Do Data Science in a GUI”. In his talk,...

The Machine Learning Reproducibility Crisis

Pete Warden is the Technical Lead on the TensorFlow Mobile Embedded Team at Google doing Deep Learning. He is formerly the CTO of Jetpac, which was...

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,...

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...

Reproducible Machine Learning with Jupyter and Quilt

In this guest blog post, Aneesh Karve, Co-founder and CTO of Quilt, demonstrates how Quilt works in conjunction with Domino's Reproducibility Engine to make Jupyter notebooks...

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. Session Summary In this Data Science...

Principles of Collaboration in Data Science

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...

Achieving Reproducibility with Conda and Domino Environments

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...

Domino raises $10.5M in funding for collaborative, reproducible data science

Today we’re announcing that we have raised $10.5 million in a funding round led by Sequoia Capital. For us, fundraising is simply a means to an...

Reproducible Research in Computational Sciences

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...

Providing Digital Provenance: from Modeling through Production

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...

Building a High-Throughput Data Science Machine

Insights on process and culture from The Climate Corporation’s Erik Andrejko This post was originally published on the O'Reilly Radar blog. Scaling is hard. Scaling data...