Subject archive for "engineering," page 6

In this Data Science Popup session, Kimberly Shenk, Director of Data Science Solutions at Domino Data Lab, explains why building models is the least important part of data scientists' jobs, and what they must focus on instead.

We have seen an explosion of interest among data scientists who want to use GPUs for training deep learning models. While the libraries to support this (e.g., keras, TensorFlow, etc) have become very powerful, data scientists are still plagued with configuration issues that limit their productivity.

In this Data Science Popup panel led by Michael Manapat, Product and Machine Learning at Stripe, we learn about practical applications of data science at Gusto, and instruction of practical data science at General Assembly. The panel members are Daniel Sternberg, Data Science and Engineering Lead at Gusto, and Kiefer Katovich, Department Chair for Data Courses at General Assembly.

This post describes how Domino uses Docker to solve a number of interconnected problems for data scientists and researchers, related to environment agility and reproducibility of work.

We've been hard at work delivering exciting new features in Domino. Our latest release included a lot, including the ability to import/share data sets across projects, easier ways to manage organizations of users, and improvements to how we host Jupyter notebooks, along with changes that make it easier to manage an on-premise deployment of Domino.

Domino provides a great way to iterate on analytical models by letting you run many experiments in parallel on powerful hardware and automatically track their results. Recently we added two powerful new features to make that workflow even easier: Run Comparisons and Diagnostic Statistics.