Cloud, MLOps, platform engineering, and the systems that run AI.
21 briefings
Spheron maps AI infrastructure into seven layers. Understanding each layer helps teams buy only what they need and avoid duplicating capabilities.
Running GPU workloads on Kubernetes requires more than scheduling pods. The Good Shell's checklist covers drivers, partitioning, queues, Spot GPUs and autoscaling.
Ahrefs reports that AI Overviews appear for 21% of all keywords but 57.9% of question queries. Understanding that distribution is the first step to appearing in them.
KodeKloud's 2026 consensus stack shows how Kubernetes-native tools can bring MLOps into production. The trick is to adopt them in stages rather than all at once.
GKE Autopilot simplifies cluster management but can hide costs for GPU-heavy and latency-sensitive ML workloads. Sometimes Standard is the cheaper and more predictable choice.
SageMaker Savings Plans and Managed Spot Training both cut costs, but they suit different workloads. Knowing when to use each is key to a balanced discount strategy.
AKS costs can drift quickly. ScaleOps recommends a focused checklist covering rightsizing, autoscaling, Spot instances, reservations and non-production scheduling.
AI hiring is shifting from research roles to production engineering. MLOps specialists are now the hardest people to recruit, and the gap says a lot about where value really sits.
Senior MLOps engineers increasingly command a premium because LLM serving requires deep knowledge of inference architecture, cost optimisation and scalable infrastructure.
Kubernetes has become the default operating system for AI workloads in 2026, driven by fractional GPU sharing, multi-cluster GPU pooling and the need to manage heterogeneous infrastructure.
Finout's guide shows how FinOps teams can govern SageMaker costs through pricing literacy, storage lifecycle rules, budget alerts and Savings Plans analysis.
KubeCon EU 2026 showed the cloud-native ecosystem embedding AI into platforms and making platforms ready for AI. The constraint now is infrastructure engineering, not model capability.
KubeCon EU 2026 will feature dedicated tracks on AI infrastructure, GPU inference and AI observability. Enterprise buyers should treat the event as a checklist, not a catalogue.
Microsoft’s preview of Gateway API support in the AKS application-routing add-on gives Kubernetes teams a path to more flexible, standardised ingress before AI API traffic grows.
A Microsoft and Nvidia demonstration shows that KV-cache-aware routing can reduce Time-To-First-Token by around 20x on Azure Kubernetes Service. The result has implications for any team running LLM inference at scale.
Idle inference endpoints, oversized training jobs and always-on notebooks are the top drivers of unexpected SageMaker spend. Fixing them is low-hanging fruit.
CERN's CNCF reference architecture combines Kubeflow, KServe, Kueue, Kyverno, Longhorn and GPU operators. The result is a practical template for any organisation running AI on Kubernetes.
Nvidia’s Vera Rubin platform promises five times the training compute of Blackwell at a fraction of the token cost. For infrastructure planners, the signal is clear: do not lock in long-term hardware commitments just yet.
The AI conversation in 2026 is shifting from general-purpose wonder to smaller models, world models and agents that augment real workflows.
Cisco's 2026 workplace forecast predicts people, data and AI agents working side-by-side. For smaller firms, the practical question is what network, identity and workflow changes to put on the roadmap now.
Eight MLOps trends are reshaping how organisations move AI from experiment to production: pipeline automation, cloud-native operations, real-time AI, observability, governance and edge deployment.
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