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Domino Data Lab is a developer of an enterprise data science platform intended to help users develop and deploy ideas with collaborative, reusable, and reproducible analysis. The company's platform is designed to help users accelerate research, speed model deployment, and enhance collaboration for code-first data science teams. The platform is also developed to work alongside popular tools and programming languages, such as Python, R, and Julia.
The company was founded in 2013 by Nick Elprin, Matthew Granade, and Chris Yang and is headquartered in San Francisco, California. Domino Data Lab customers have included Lockheed Martin, Johnson & Johnson, Moody's Analytics, VMware, Bayer, Red Hat, and Allstate. The company has been backed by venture capital firms, including Sequoia Capital, Bloomberg Beta, Coatue Management, Dell Technologies Capital, Highland Capital Partners, and In-Q-Tel.
Domino Data Lab's MLOps platform is developed for enterprises so users can utilize data science at scale to resolve challenges around infrastructure friction, productionization challenges, and a lack of collaboration. Some features of the platform include the system of record, which allows users to capture data science work in a central repository, enabling teams to find, reproduce, and reuse work. As well, this allows users to compound knowledge with reusable code, artifacts, learnings from previous experiments, integrated project management capabilities, and the ability to replicate environments.
Example page of Domino's integrated model factory feature.
Another feature of the platform is called the integrated model factory, which is intended to support the data science lifecycle from ideation to production. Through this feature, users can explore data, train machine learning models, validate, deploy, and monitor these ideas and these models in production. This is intended to enable processes and workflows, which can be repeated for faster model production, automated monitoring, retraining, and republishing.
The platform offers a self-service infrastructure portal that allows users to automate time-consuming DevOps tasks required for data science to work at scale. Users can generate development sandboxes capable of being pre-loaded with preferred tools, languages, and compute power and frameworks. Further, this enables users to jump between environments, bring in data, and compare experiments to deploy and iterate upon models.
