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Best Strategies for Cloud Native Cost Optimization
The rise of cloud-native technologies—containers, microservices, serverless computing and Kubernetes—has pushed companies to think more carefully about how to control the costs associated with these technologies. As the number of organizations deploying ...
Docker Hub vs. Private Registries: Security Tradeoffs
In the race to accelerate software delivery, Docker Hub has become a default starting point for developers and organizations alike. It offers convenience, accessibility, and a vast ecosystem of pre-built images that ...
Red Hat Readies an MCP Server to Help LLMs Manage Kubernetes
Red Hat is building an open source MCP server extension to help developers and administrators manage Kubernetes and Red Hat’s own OpenShift Kubernetes distribution. The extension can help AI assistants like Visual ...
Kubeflow’s Graduation Is a Vote for Kubernetes as the AI Control Plane
Kubeflow’s CNCF graduation signals growing confidence in Kubernetes as a common control plane for production AI workloads, from training and pipelines to governance and inference ...
Alan Shimel | | agentic AI, AI infrastructure, AI lifecycle, AI platform, AI Workloads, cloud native AI, cncf, Distributed Training, enterprise AI, GPU scheduling, KServe, Kubeflow, Kubeflow graduation, Kubeflow Pipelines, Kubeflow Trainer, kubernetes, Kubernetes AI, MLOps, OpenTelemetry, platform engineering
CNCF Graduates Kubeflow for Production AI on Kubernetes
The Cloud Native Computing Foundation has graduated Kubeflow, giving the open source AI and machine learning platform CNCF’s highest maturity designation as enterprises move more AI workloads into production. Kubeflow runs on ...
How Base Images Impact Software Supply Chain Security in Kubernetes
As organizations scale their Kubernetes environments, the software supply chain becomes increasingly complex, interconnected, and vulnerable. One of the most overlooked yet foundational components of this supply chain is the base image ...
The Hidden Cost of “Just Works” Load Balancing in a Service Mesh
If you’re running a multi-AZ Kubernetes cluster with a service mesh on top, there’s a good chance you’re paying a tax you never signed up for, and it won’t show up as ...
How We Cut Kubernetes Deployment Validation From 45 Minutes to 2 minutes
There is a moment every release engineer knows well. The CI/CD pipeline turns green. The deployment job reports success. Everyone exhales for a second and thinks, “Okay, the release is done.” But ...
Docker Desktop Gets a Hypervisor of its Own
Docker is bringing full backend parity across all of its Docker Desktop editions, ensuring that macOS, Windows, and (eventually) Linux users get the same performance and polish. The company unveiled a new ...
Kubernetes Wasn’t Built for GPUs. Make It Behave
Kubernetes counts whole GPUs and treats pods as disposable. An LLM pod is neither. Share the silicon with MIG/MPS/time-slicing and stop paying for idle ...
Sneha Gullapalli | | A100, AI infrastructure, AI Workloads, cloud native AI, Dynamic Resource Allocation, GPU autoscaling, GPU cost reduction, GPU optimization, GPU partitioning, GPU sharing, GPU time-slicing, GPU utilization, H100, Karpenter, KServe, Kubernetes DRA, Kubernetes GPU scheduling, LLM Inference, model caching, multi-instance GPU, NVIDIA GPU Operator, NVIDIA MIG, NVIDIA MPS, scale-to-zero, VRAM

