In October 2017, we published an article introducing Data Science Workbench (DSW) , our custom, all-in-one toolbox for data science, complex geospatial analytics, and exploratory machine learning. It centralizes everything required to perform data preparation, ad-hoc analyses, model prototyping, workflow scheduling, dashboarding, and collaboration in a single-pane, web-based graphical user interface.
In this article, we reflect on the evolution of DSW over the last 3 years. We review our journey by looking at how DSW’s usage has evolved to include and supercharge the workflows of more than just data scientists, dive into how the choices we made when designing the platform have helped us scale, offer an in-depth look at our current approach and future goals towards democratizing data science, and also share some lessons that we learned along the way.
Since DSW’s launch in 2017, we’ve seen usage skyrocket. Over 4000 monthly active users are leveraging DSW for their data science, complex analytics, and exploratory machine learning needs. Along with the growth, we also see more diverse customer needs. Teams are leveraging DSW for increasingly complex applications, such as pricing, safety, fraud detection, and customer support, among other foundational elements of the trip experience.