Achieved highest scores possible in platform, solution roadmap, partners, and market awareness criteria
Domino Data Lab, provider of an open data science platform, today announced they’ve been named a leader in the Forrester Research, Inc. report: The Forrester Wave™: Notebook-based Predictive Analytics and Machine Learning Solutions, Q3 2018.” The report evaluated nine vendors based on three criteria buckets: current offering, strategy, and market presence.
In addition to receiving the highest scores possible in the platform, solution roadmap, partners, and market awareness criteria, Domino Data Lab scored among the top three in the market presence category.
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According to the Forrester report, “Domino Data Lab lets you conduct experiments with your tool of choice. Domino Data Lab is all about making your data science teams more productive by working with them rather than forcing them to use just one notebook interface. Data scientists love their different tools, so Domino lets users code in Jupyter, Zeppelin and R Studio, but also in proprietary tools like SAS and DataRobot. It enables this through a unified platform that enables self-service provisioning of resources and allows teams to both standardize their environments and modify them when experimentation is called for.”
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“We’re honored by our placement as a Leader in The Forrester Wave report. In our opinion, it validates that we’re delivering on our mission to help companies become model-driven by helping them embed data science throughout the business,” said Nick Elprin, CEO at Domino Data Lab. “We’re hyper-focused on creating a data science platform supporting the entire model management lifecycle, from model development through model production and ongoing governance, and will continue on this path to help Domino clients elevate data science from a narrow technical skill to an organizational capability.”
Domino empowers model-driven business with its open, unified platform that allows data science organization to build, validate, deliver, and monitor models at scale. This accelerates research, sparks collaboration, increases iteration speed, and removes barriers to operationalization so that data science teams can deliver impactful models.
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