Allowing data scientists and teams the ability to track, compare, explain, reproduce ML experiments.
With Dell EMC PowerEdge reference architectures, organizations can deploy artificial intelligence workload-optimized rack systems approximately 6-12 months faster than it would have taken to design the correct configurations and deploy the solution. Organizations can now rely on architectures that are tested and validated by our Dell engineers and know that services are available when and where you need them.
“Orchestrating and managing the stack for enterprise data science teams is a huge pain point for many of our customers,” said Gideon Mendels, Co-founder/CEO, Comet. “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 is a meta machine learning experimentation platform allowing users to automatically track their metrics, hyperparameters, dependencies, GPU utilization, datasets, models, debugging samples, and more. By leveraging Comet, data science teams produce faster research cycles, and more transparent and collaborative data science. Comet also provides a built-in hyperparameter optimization service, interactive confusion matrices, full code tracking and reproducibility features. Comet on-premise installations can support teams of any size, from a single machine to distributed microservices.
“This is one of those products that makes you question how you functioned without it. Comet gives data science teams all the automation and productivity features they need, but that they never get around to developing themselves,” says Phil Hummel, Senior Principal Engineer and Distinguished Member of Technical Staff, Dell EMC.
The Reference architecture utilizes Dell EMC AI-Enabled Kubernetes solution backed by Canonical’s Charmed Kubernetes and Kubeflow that complies with all requirements of the AI workload.
The solution includes 100% upstream Kubernetes latest code wrapped into easily consumable packages and supported by Canonical.
Dell EMC and Comet’s reference architecture and data science team user story illustrates how our joint solution can provide teams the tools and infrastructure they need to manage their machine learning workflows—data storage, experimentation and model building, and deployment—while providing flexible and robust deployment options as teams scale.
To learn more about Dell EMC’s AI-Enabled Kubernetes Cluster options, read more here or reach out to Dell EMC directly to schedule a call. To learn more about Comet’s meta-machine learning platform, read more about Comet here or contact Comet’s customer solutions team for more information or to schedule a demo.
Read the full whitepaper here that covers (a) using Comet with Dell EMC AI-enabled infrastructure and (b) technical installation instructions.