The advent of artificial intelligence heralds a new golden era in computing. The prospects of implementing AI universally, spanning various societies and the extensive range of technology, are extraordinary.

In the bustling heart of Paris, the KubeCon + CloudNativeCon Europe conference unfolded as a landmark event, showcasing the revolutionary tide of cloud-native applications seamlessly entwined with the dynamism of AI and machine learning. In a recent discussion at the conference with Jeremy Eder (pictured, left), chief AI/ML strategist at Red Hat Inc., and Jennifer Vargas (right), principal product marketing manager at Red Hat, the focus was on the dynamic interplay between Kubernetes, AI, and machine learning.

Eder and Vargas spoke with theCUBE’s Savannah Peterson and Rob Strechay at KubeCon, during an exclusive broadcast on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed the integration of Red Hat’s contributions to Kubeflow within the Kubernetes ecosystem, the importance of open governance in open-source projects, and how customer feedback shapes the adoption and advancement of AI and machine learning workloads in cloud computing. (* Disclosure below.)

Embracing Kubeflow in the Kubernetes ecosystem

Exploring the world of cloud-native applications, Kubeflow stands out as a transformative project under the CNCF umbrella, reshaping workflows for data scientists and machine learning operations practitioners, according to Eder. This remarkable project is more than just a tool; it’s an umbrella project that encompasses a variety of sub-projects, each integral to optimizing operations across hybrid cloud environments.

“Our relationship with Kubeflow started in August of last year, a little bit tentatively, and we’ve ramped up significantly. They welcomed us with open arms, and we recently put out a blog detailing what our journey has been so far,” Eder said. “ Some of the key points … that we wanted to do a release, be the release manager, do the dirty work, prove that we know how the teams talk to each other, how the components interconnect, how CI works.”

As a comprehensive solution, Kubeflow is not only enhancing operational efficiencies, but also pioneering a new approach to managing data science and machine learning operations, marking its importance in the evolving landscape of cloud-native technologies, according to Eder.

“One of the things that has been very impactful is seeing the way that many of our customers, because many of our customers are OpenShift customers, have been using the platform,” Vargas said. “They have adopted DevOps practices. And one of the great things to see them is doing that evolution now to AI/ML, understanding that the best way to do this is to expand their current infrastructure and treat the AI workload as another workload on top of Kubernetes.”

Integration challenges and customer feedback

Feedback from customers has been a driving force in shaping the development and integration of these technologies. The integration of AI and machine learning into cloud computing, particularly within the Kubernetes ecosystem, represents a significant shift in how technology is being developed and deployed, according to Eder.

“What a vendor like Red Hat does is capture all of those anti-patterns and pivot them in product so that you can become productive as quickly as possible,” Eder explained. “Our platform has one goal, time to value. And so whatever we can do to remove roadblocks to help the key piece here is making sure that the data scientists and machine learning practitioners  are tightly coupled.”

Here’s the complete video interview, and tune into SiliconANGLE’s and theCUBE Research’s ongoing coverage of KubeCon + CloudNativeCon Europe here.

(* Disclosure: Red Hat Inc. sponsored this segment of theCUBE. Neither Red Hat nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)

Photo: SiliconANGLE