Dessa, a Canadian artificial intelligence (AI) start-up previously known as Deeplearni.ng, announced today the availability of Atlas 2.0, one half of its Foundations suite of tools for building, deploying, and maintaining enterprise-grade machine learning (ML) models at scale.
Foundations Atlas is a suite of tools for building and deploying ML models. Its complement, Foundations Orbit, is a suite of tools for monitoring and maintaining ML models in production.
Atlas was developed to “transform the time-consuming and frustrating process of managing [ML] experiments into something that’s easy and even enjoyable, also allowing for insights you might not have otherwise,” says Alex Krizhevsky, the company’s principal ML architect. (He is one half of the duo of graduate students at the University of Toronto who, together with Geoffrey Hinton, cracked the ImageNet competition in 2012 and ushered in a new era of AI and ML with deep learning/neural networks.)
Specifically, Atlas addresses the following five ML frustrations:
In addition to fixes and improvements, version 2.0 of Atlas includes two new features:
Read more on the company’s Medium page.
Atlas 2.0 is available as a community edition on Product Hunt.
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