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...
Humans in the Loop
This guest blog post from Paco Nathan dives into how people and machines collaborating together to perform work is real and not science fiction. Paco Nathan is...
New G3 Instances in AWS – Worth it for Machine Learning?
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...
Data Science != Software Engineering
Domino’s guide, “What Engineering Leaders Need to Know About Data Science”, provides insights to help engineering leaders increase data science productivity and decrease engineering time spent...
Model Deployment Powered by Kubernetes
In this article we explain how we’re using Kubernetes to enable data scientists to deploy predictive models as production-grade APIs. Background Domino lets users publish R...
Data Science on AWS: Benefits and Common Pitfalls
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...
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...