Deep Learning on GPUs without the Environment Setup
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...
Enabling Data Science Agility with Docker
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...
Image diffing with CSS tricks
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,...
Faster model tuning and experimentation
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....
Reflections on “buy vs build”
“Buy vs build”, “not-invented-here syndrome” and even “invented-here-syndrome” have been written about extensively. I want to share a few reflections on the topic, based on my...
Building an open product for power users
This post describes our engineering philosophy of building an “open” product, i.e., one that supports existing tools and libraries, rather than building our own custom version...
Cloud Security: The right way to worry
Here’s a question we hear a lot: We’re not that comfortable with the cloud from a security perspective -- can you install Domino on premise? The...
A mongo-based cache plugin for Play
A quick engineering-related post: we built a cache plugin for Play that uses capped collections in Mongo. It's available on Github if you'd like to use...
R Notebooks in the Cloud
We recently added a feature to Domino that lets you spin up an interactive R session on any class of hardware you choose, with a single...
Management lessons from software engineering
As I've evolved from being an individual software developer to managing teams and starting a company—a data science platform—I continue to notice parallels between the principles...