Subject archive for "domino-product," page 4
The team at Domino is proud to be named a Visionary for the second year in a row in Gartner’s Magic Quadrant for Data Science and Machine Learning Platforms. It is exciting to see that companies and industry analysts are increasingly realizing the value of a data science platform, and we’re honored to be recognized. It was particularly gratifying to hear feedback from our customers synthesized and channeled through Gartner’s report.
By Mac Steele3 min read
This post is a recap of the common questions Domino answered in the booth at Strata New York. We answered questions about access to EC2 machines, managing environments, and model delivery.
By Domino Data Lab7 min read
We recently released new functionality that provides first-class integration between Domino and git. This post describes the new feature, and describes our perspective on the unique requirements of version control in the context of data science—as distinct from software engineering—workflows.
By Eduardo Ariño de la Rubia5 min read
More than two years ago, we wrote about the misguided fear of the cloud among many enterprise companies. How quickly things change! Today, every enterprise we work with is either using the cloud or in the process of moving there. We work with companies that insisted, just two years ago, that they “can’t use the cloud” — and are now undertaking strategic initiatives to have “real work in AWS by the end of 2017.” We see this happening across industries including finance, insurance, pharmaceuticals, retail, and even government.
By Nick Elprin4 min read
Managing “environments” (i.e., the set of packages, configuration, etc.) is a critical capability of any Data Science Platform. Not only does environment setup waste time on-boarding people, but configuration issues across environments can undermine reproducibility and collaboration, and can introduce delays when moving models from development to production.
By Eduardo Ariño de la Rubia8 min read
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