Pump up data science productivity with a modern workbench

Today’s companies are looking for ways to solve increasingly complex business challenges with data science.

Unfortunately, efforts to scale data science productivity across the enterprise will fail when there is a lack of scalable and flexible tooling and workflows that allow large teams of data scientists to systematically experiment and collaborate on projects. Why? Most data science workbenches don’t deliver three core capabilities simultaneously, resulting in stalled attempts to increase productivity.

Accelerate your data science productivity with capabilities used by many Fortune 100 companies to deliver data science at enterprise scale.

Download this compact guide to learn:

• The current shortcomings of legacy workbenches
• The unique requirements for data science productivity
• Key capabilities for a next-generation workbench
• How to align enterprise-scale data science programs with business needs

This paper represents best practices Domino has learned from years of experience working with data science leaders at companies such as Allstate, Bayer, Dell, and Moody’s Analytics.