Subject archive for "data-engineering," page 3

Aneesh Karve, Co-founder and CTO of Quilt, visited the Domino MeetUp to discuss the evolution of data infrastructure. This blog post provides a session summary, video, and transcript of the presentation. Karve is also the author of "Reproducible Machine Learning with Jupyter and Quilt".

We benchmarked AWS’s new G3 instances for deep learning tasks and found they significantly outperform the older P2 instances. The new G3 instances are now available for use in Domino.

In this Data Science Popup session, W. Whipple Neely, Director of Data Science at Electronic Arts, explains why data scientists have responsibilities beyond just data science.

More than two years ago, we wrote about the misguided fear of the cloud among many enterprise companies. How quickly things change! Today, every enterprise we work with is either using the cloud or in the process of moving there. We work with companies that insisted, just two years ago, that they “can’t use the cloud” — and are now undertaking strategic initiatives to have “real work in AWS by the end of 2017.” We see this happening across industries including finance, insurance, pharmaceuticals, retail, and even government.

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.