Dell EMC and Comet have announced a collaboration with a reference architecture for data science teams seeking to harness the power of the Dell EMC infrastructure in concert with Comet's meta machine learning platform.
By leveraging Dell EMC PowerEdge reference architectures, organizations can now deploy artificial intelligence workload-optimized rack systems approximately 6-12 months faster than before. Moving forward, organizations can rely on architectures Dell engineers have tested and validated.
"Orchestrating and managing the stack for enterprise data science teams is a huge pain point for many of our customers," Gideon Mendels, co-founder and CEO, Comet, said.
"Dell EMC's Kubeflow and Kubernetes solutions are best-in-class and an excellent choice for any data science team looking to build a robust and scalable ML platform."
Comet, the industry-leading meta machine-learning experimentation platform, allows data scientists and teams to track, compare, explain and optimize both experiments and models. With thousands of users, including a number of Fortune 100 companies, Comet not only provides insights and data that allow teams to build better AI models, but it also improves productivity, collaboration and visibility among teams.
"This is one of those products that makes you question how you functioned without it. Comet gives data science teams the automation and productivity features they need, but that they never get around to developing themselves," said Phil Hummel, senior principal engineer and distinguished member of technical staff, Dell EMC.
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The reference architecture uses Dell EMC’s AI-Enabled Kubernetes solution. This is backed by Canonical's Charmed Kubernetes and Kubeflow solutions, and complies with all requirements set out by the AI workload. The solution also includes 100% upstream Kubernetes latest code, all of which are wrapped into easily consumable packages and supported by Canonical.
This partnership between Dell EMC and Comet illustrates how a joint solution among two organizations can provide teams with the tools and infrastructure they need to be successful. Not only will data teams be able to manage their machine-learning workflows, but they will now also have flexible and robust deployment options as teams scale.
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