Subject archive for "it-leaders," page 2

Data Science

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 thousands of temporary models to find the optimal configuration. Even for small data sets, this could take hours to process.

By Eduardo Ariño de la Rubia6 min read

Data Science

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 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.

By Nick Elprin4 min read

Data Science

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 result, the scale of projects continues to grow. Collaboration is critical because it enables teams to take on larger problems than any individual. It also allows for specialization and a shared context that reduces dependency on "unicorn" employees who don't scale and are a major source of key-man risk. The problem is that collaboration is a vague term that blurs multiple concepts and best practices. In this post, we clarify the differences between repeatability, reproducibility, and whenever possible the golden standard of replicability. By establishing best practices of frictionless in-team and cross-team collaboration, you can dramatically improve the efficiency and impact of your data science efforts.

By Eduardo Ariño de la Rubia17 min read

Data Science

Deep Learning on GPUs without the Environment Setup in Domino

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.

By John Joo3 min read

Data Science

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 parties, but it was particularly gratifying to hear feedback from our customers synthesized and channeled through Gartner’s report. We were most proud of the feedback we got in three areas: our product, our customer service, and our sales team.

By Nick Elprin2 min read

Data Science

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 a “data science platform” without describing the features that make platforms so valuable. So we wanted to share our vision for the core capabilities a platform should have in order for it to be valuable to data science teams.

By Nick Elprin7 min read

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