tag: Ai-Workloads · 4 items
- Platform/SRE — Learn: Kubernetes is formalizing AI workload conformance standards under SIG Architecture, which shapes how AI inference and training workloads will be certified to run on Kubernetes; no operational change needed today, but worth tracking as conformance requirements may influence cluster configuration choices for AI workloads.
- CI/CD — Skip
- Leader — Learn: Kubernetes’s elevation of AI Conformance to a formal SIG subproject signals the ecosystem is converging on standards for AI workloads, useful context when evaluating long-term platform strategy for AI infrastructure.
- Platform/SRE — Learn: Useful framing on how GPU-bound and bursty AI workloads strain the classic HPA/VPA/cluster-autoscaler division; no version change or deadline, but informs how you’d architect autoscaling for AI teams onboarding to the cluster.
- CI/CD — Skip
- Leader — Learn: Useful context for platform strategy as teams push AI/ML workloads onto shared Kubernetes; no near-term decision or cost/vendor change implied, but shapes how you’d evaluate autoscaling capabilities when reviewing cluster investments.
- Platform/SRE — Learn: Survey data confirms Kubernetes as the dominant platform for GenAI workloads; useful for validating architectural direction but no operational change required.
- CI/CD — Skip
- Leader — Learn: CNCF survey finding that 66% of GenAI-hosting orgs run on Kubernetes is useful context for platform strategy and build-vs-buy conversations around AI infrastructure.
- Platform/SRE — Skip
- CI/CD — Skip
- Leader — Learn: Opinion piece on AI workload placement and sovereignty considerations — useful for shaping platform strategy thinking, but no concrete decision or deadline is present.