Data science should never be a solo or individual sport. In fact, building advanced models wouldn’t even be possible without the use of third-party libraries, best practices, and in some cases, the scale of public cloud infrastructure.

Furthermore, for many of these machine learning model types, the state of the art is a fast-moving target. No single data scientist can keep up with every influential research paper published at KDD, ICML, or NeurIPS. And there are many shareable lessons whenever this state of the art is applied to a new problem within any given organization. Often, the most important part of a modeling process is iterating on ideas with your peers. 

Yet most teams lack a systematic way to collaborate. Most options force teams to rely on productivity tools that are not purpose built for modeling, or standardize on an end-to-end system that limits the way they use libraries, infrastructure or even data. 

To meet this need, SigOpt was designed on the following premises:

• Teams should have the freedom to choose different libraries based on their modeling needs without compromising their ability to share and reuse each other’s work
• Team members should have an efficient way to share and analyze their past trials, experimentation, and successes in a reproducible way with a teammate or manager
• Teams should be able to easily collaborate across organizations or lines of business with proper privacy and controls permitting

A SigOpt experiment page: old, then new. Note the extensible sidebar navigation, summary in the header, and streamlined navigation options.

You’ll also notice that SigOpt’s web interface has a new look and feel. We rearranged the interface, because we found that most of our customers almost immediately jump to the Analysis page from the Summary view, for lots of intuitive and useful interactive charts and graphs. But we’re always assessing customer feedback, and the new UI makes way for more advanced features as our product and engineering teams respond to new customer needs from the growing set of industries that we serve.

SigOpt’s Experiments interface through the years: at the beginning, yesterday, and today. Useful observation information and navigation options are now more readily available.

In addition to our optimization and experimentation platform, SigOpt also offers professional services from our Customer Success team, comprised of dedicated machine learning engineers. Learn how SigOpt can help your teams collaborate on building and tuning the best models, and schedule a demo and consultation today.

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