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Domino Data Lab Announces Domino 2.0, the Next Generation of its Data Science Platform


Domino Data Lab, a visionary in data science, today announced Domino 2.0, the next generation of its Data Science Platform. The San Francisco-based company enhanced the platform based on feedback from customers including Allstate, DBRS, Instacart, and Monsanto among others.

Already recognized as a visionary by Gartner in the 2017 Magic Quadrant for Data Science Platforms, Domino 2.0 enhances the end-to-end research process for teams across industries, with innovations in four notable areas:

  • Model Deployment:a complete overhaul of Domino’s “API Endpoints” feature allows it to support web-scale, low-latency production workloads, and introduces advanced capabilities such as A/B testing of models.
  • Environment Management:self-service compute environments are now shareable and revisioned, letting data scientists focus on their value-add work instead of worrying about infrastructure, package setup, configuration, or connection to their big data stores.
  • Experiment Tracking and Meta-Analysis:a new user experience makes it possible to find and review past results and visualize progress over time.
  • Result Publication for Business Users:reports, interactive applications and dashboards make it easier to put data science results into the hands of the business.

“The product innovations in Domino 2.0 are the result of feedback and lessons learned working with leading data science teams over the last two years, and they have led to the most innovative data science platform in the market today,” according to Nick Elprin, CEO and co-founder of Domino Data Lab. “The best data science teams are successful because they have a disciplined process and iterate quickly, not because of any one specific package or model. We’re delighted to give organizations a platform that enables collaboration, reproducibility and more rapid iteration because we’ve seen how much that accelerates innovation.”

Domino’s Data Science Platform provides a more robust research process and gives practitioners a workbench with self-service scalable compute environments, letting them test ideas faster. By automatically tracking work in one place, it enables collaboration, reuse of work and reproducibility — which accelerate innovation and reduce risk. Data scientists and researchers can productionize their work — either as APIs, reports or dashboards — so data science can have a more rapid impact on the business.

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