tag: Ai-Ml · 4 items
- Platform/SRE — Learn: Kubernetes 1.37 is a GA minor release (EOL 2027-10-28) with no urgent upgrade pressure, and its headline features — gang scheduling, Workload-Aware Preemption, CompositePodGroup — are Beta or newly introduced, capping this at Learn. Worth tracking for teams running AI/ML or large batch workloads on Kubernetes as these APIs stabilize toward GA.
- CI/CD — Skip
- Leader — Learn: The maturing Workload-Aware Scheduling APIs (gang scheduling, hierarchical PodGroups) signal that upstream Kubernetes is building native primitives for AI/ML batch infrastructure that previously required external schedulers — worth incorporating into the AI/ML platform strategy roadmap as these graduate to GA.
- Signals: Kubernetes 1.37 EOL 2027-10-28
- Platform/SRE — Learn: Describes a multi-tenant GPU pooling architecture on Kubernetes for concurrent AI workloads; useful design reference if the org is evaluating shared GPU infrastructure, but no GA tooling or deadline makes this actionable today.
- CI/CD — Skip
- Leader — Learn: Offers a mental model for AI infrastructure as a shared organizational capability; relevant if evaluating whether to build a centralized GPU platform versus per-team provisioning.
- Platform/SRE — Skip
- CI/CD — Skip
- Leader — Learn: Relevant if the org operates in India with data-residency requirements and uses Bedrock — this expands the compliant model options available without leaving AWS, worth noting for AI strategy reviews.
- Platform/SRE — Learn: Useful reference for designing storage architectures that support AI/ML workloads on cloud-native infrastructure, but no operational changes required and nothing currently running is affected.
- CI/CD — Skip
- Leader — Learn: Shapes strategic thinking on how to architect platforms for AI/ML workloads at scale; no immediate decision or vendor action required.