Subject archive for "data-science-leaders," page 9

This week, Domino’s Chief Data Scientist, Eduardo Ariño de la Rubia, presented a webinar: Machine Learning at Scale with Amazon's X1 Instance. If you missed the live webinar or would like to watch it again, you can find a recording below:

When you have at your disposal 128 cores and 2TB of RAM, it’s hard not to experiment and attempt to find ways to leverage the amount of power that is at your fingertips. We’re excited to remind our readers that we support Amazon’s X1 instances in Domino, you can do data science on machines with 128 cores and 2TB of RAM — with one click:

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.

“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 observations both as an engineering manager (where I had to decide whether to build or buy solutions) and more recently as a founder selling a platform to other companies.

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 of existing functionality. Aside from letting our developers be more productive, we’ve found this approach makes our users much more productive — especially power users, who are especially important to us.

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 answer is yes (we have both an on-premises and cloud-hosted version of the Domino data science platform because that’s what clients want) but we think the central assumption of that question deserves further consideration, because it’s often wrong.