Domino Data Lab recently announced the release of the latest version of its flagship data science and machine learning offering featuring the addition of Domino Data Monitor (DMM) on the major clouds. Domino version 4.6 can automatically compute data drift and model quality across billions of daily predictions via the company’s new Elastic Monitoring Engine. DMM can also now connect directly to Amazon S3 and Hadoop Compatible File Systems (HCFS), which include Azure Blob Store, Azure Data Lake (Gen1 and Gen2), Google Cloud Storage, and HDFS.
Domino Data Lab is an enterprise data science platform that allows data scientists to build and run predictive models. The 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.
Version 4.6 introduces deployable access to Ray.io and Dask distributed compute frameworks. Distributed compute allows data scientists to process large volumes of data for machine learning and other mathematical calculations like deep learning. New DevOps-free support for the major clouds also makes it easy for data science teams to leverage pre-packaged machine learning libraries. The release marks Domino’s certification of Amazon EKS and adds new Git Repository Creation, Read/Write Domino Datasets, and Single Sign-On functionality.
In a media statement on the news, Domino Data Lab CEO Nick Elprin said: “Successful model-driven businesses have thousands of models driving critical business processes. By helping our customers detect drift faster and across more of their models, Domino is reducing risk while freeing up data scientists’ time for new research.”
Read Domino 4.6: Greater Confidence & Flexibility for Model-Driven Businesses in the company’s blog to learn more.
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