Domino Data Lab recently unveiled version 3.0 of its flagship data science platform featuring Domino Launchpad, a module designed to address operational issues in model production. Launchpad enables data science teams to increase iteration speed on these models to ensure they have a higher chance of immediately impacting the business.
Domino Data Lab is an enterprise data science platform that allows data scientists to build and run predictive models. Its product helps organizations with the development and delivery of these models via infrastructure automation and collaboration. Domino provides users access to a Data Science Workbench that provides open source and commercial tools for batch experiments, as well as Model Delivery so they can publish APIs and web apps or schedule reports.
Domino Launchpad features automatic infrastructure provisioning via Docker and support for tools like Shiny, Flask and Dash. The tool includes a single portal for discovering model products and usage data to ensure feedback loops and validate a model’s impact. There’s also automatic model versioning and full reproducibility of experimental history for hastened model iteration.
In a media statement, the company’s CEO Nick Elprin said: “Domino 3.0 tackles model operations challenges surrounding model delivery and ongoing iterations of production models. By streamlining the model deployment process and facilitating faster, easier iterations throughout the model management cycle, the latest Domino functionality helps data science leaders ensure that their investments in data science are yielding tangible business impact.”
Domino raised a considerable amount of venture capital in August to expand its model management capabilities, one of the top-five largest funding rounds of the year. The company was also included in Gartner’s 2018 Magic Quadrant for Data Science and Machine-Learning Platforms, as well as the CRN Big Data 100.
To learn more about Domino 3.0, you can register for a live webinar that will take place on November 14.
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