Dell EMC and Comet have unveiled a Kubernetes reference architecture for data science teams using the their artificial intelligence (AI)-enabled infrastructure and machine learning platform.
Using the Dell EMC PowerEdge reference architectures, organisations can deploy AI workload-optimised rack systems up to 12 months faster than it would have taken to design the correct configurations and deploy the solution.
“Orchestrating and managing the stack for enterprise data science teams is a huge pain point for many of our customers. 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,” said Gideon Mendels, Co-founder and Chief Executive Officer of Comet.
Comet’s meta machine learning experimentation platform lets users automatically track their metrics, hyperparameters, dependencies, GPU utilisation, datasets, models, and debugging samples..
By leveraging Comet, data science teams produce faster research cycles, and more transparent and collaborative data science.
Comet also provides built-in hyperparameter optimisation service, interactive confusion matrices, full code tracking and reproducibility features. On-premise installations support teams of any size.
“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 at Dell EMC.