AI Infrastructure on Kubernetes
The rise in usage of cloud computing resources and container management platforms for executing AI (Artificial Intelligence) and ML (Machine Learning) workloads has led many engineers and companies to question the suitability and effectiveness of Kubernetes’ resource management and scheduling to meet the growing requirements of these workloads.
So why’s that? What patterns, architectures, and procedures has led these companies and engineers to this problem of scaling ML platforms on Kubernetes? And what kind of solution could we apply to help solve those problems?
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