The Role of K8s in ML – The AI Times EP 4

July 27, 2023

K8s has become utterly essential in cloud computing, but is it getting the same traction in machine learning? Can Kubernetes make machine learning easier, or is it too low-level for data scientists unless it’s abstracted away? What’s the perfect interface for Kubernetes, and how can we make it work for us in AI/ML?
• Do data scientists ever need to deal with K8s? Should they or should that always be a separate team?
• Is native k8s enough, or do we need specialized tools layered on top?
• What are some of the top platforms built on top of K8s?
• Has Kubeflow gained any traction or is it falling behind the rest of the industry as a toolset?
• Should companies try to manage their own k8s, or are they better off with a managed service?
• Is Kubernetes necessary for scaling, or are there alternatives that make a lot of sense for industrial-scale AI?

Share some ❤
Categories: The AI Times
starts in 10 seconds