Tag: IT Leaders

Put Models at the Core of Business Processes

At Rev, Nick Elprin, Domino's CEO, continued to provide insights on managing data science based upon years of candid discussions with customers. He also delved into...

Avoiding a Data Science Hype Bubble

In this post, Josh Poduska, Chief Data Scientist at Domino Data Lab, advocates for a common taxonomy of terms within the data science industry. The proposed...

Best Practices for Managing Data Science at Scale

We recently published a practical guide for data science management intended to help current and aspiring managers learn from the challenges and successes of industry leaders....

What Your CIO Needs to Know about Data Science

What would you rather be doing? Data science or DevOps? As a data scientist, your CIO may hear from you that model deployment is a challenge...

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...

The Cost of Doing Data Science on Laptops

At the heart of the data science process are the resource intensive tasks of modeling and validation. During these tasks, data scientists will try and discard...

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...

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...

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...

Our Customers Said it Best, as Domino Named a Visionary By Gartner

The team at Domino is proud to be named a visionary in Gartner’s Magic Quadrant for Data Science Platforms. It’s nice to get recognized by third...

Data Science Platform: What is it? Why is it Important?

As more companies recognize the need for a data science platform, more vendors are claiming they have one. Increasingly, we see companies describing their product as...

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...