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Unlocking High-Performance AI/ML in Kubernetes with DRANet and RDMA

DraNetslaps networking woes straight out the door. It natively handles RDMA in Kubernetes, so you can toss those convoluted scripts. Now in beta and weighing only 50MB, it offers deployments that are lean, speedy, and unyieldingly secure... read more  

Unlocking High-Performance AI/ML in Kubernetes with DRANet and RDMA
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Critical NVIDIA Container Toolkit Flaw Allows Privilege Escalation on AI Cloud Services

A critical container escape vulnerability (CVE-2025-23266) in NVIDIA Container Toolkit poses a severe threat to managed AI cloud services, earning a CVSS score of 9.0 out of 10.0. This flaw allows37%of cloud environments to potentially be accessed by attackers using a three-line exploit, enabling co.. read more  

Critical NVIDIA Container Toolkit Flaw Allows Privilege Escalation on AI Cloud Services
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The Evolution of Virtualization Platforms: The Rise of Managed Services and Local Providers’ Edge Against Hyperscalers

Cozystackwants local cloud providers to flex by deliveringKubernetes-based managed serviceswithout breaking a sweat. Who needs hyperscalers anyway? Built on open-source goodness, it ditches vendor lock-in, giving these providers the freedom to roll out next-gen infrastructures in style... read more  

The Evolution of Virtualization Platforms: The Rise of Managed Services and Local Providers’ Edge Against Hyperscalers
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Building scalable secrets management in hybrid cloud environments

GitGuardian's 2024 reportsounds the alarm:23 million secrets slipped through leaks in 2023. A whopping 70% hung around for months. Talk about a security nightmare! EnterHashiCorp VaultandAkeyless. These tools mastered the multi-cloud juggling act and automated secrets management. Result? A satisfyin.. read more  

Building scalable secrets management in hybrid cloud environments
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GitOps continuous delivery with ArgoCD and EKS using natural language

ArgoCD MCP Serverteams up withAmazon Q CLIto shake up Kubernetes with natural language controls. Finally, GitOps that even the non-tech crowd can handle. Kiss those roadblocks goodbye. No more brain strain from Kubernetes. Now, plain language syncs apps, reveals resource trees, and checks health sta.. read more  

GitOps continuous delivery with ArgoCD and EKS using natural language
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Upcoming changes to the Bitnami catalog

Bitnamiclears out the virtual cobwebs by tucking its oldDebian-based imagesinto a digital time capsule, also known as theLegacy repository. It throws a friendly nudge to devs: get with the times and swap to the "latest" images. In production-ville, serious users should hitch a ride on theBitnami Sec.. read more  

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Setting up Prometheus Stack on Kubernetes

Devtronis Kubernetes monitoring on overdrive. It ropes inPrometheusandGrafana, automates the pesky setup, and shoots real-time insights straight into a slick UI. Effort? Minimal. Results? Maximal... read more  

Setting up Prometheus Stack on Kubernetes
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Automated Kubernetes Threat Detection with Tetragon and Azure Sentinel

Kubernetes security tools usually drop the ball. Enter the dynamic duo:Tetragonwielding eBPF magic for deep observability, and smart notifications for sniper-precise alerts.Fluent Bitpairs withAzure Logic Appsin an automated setup so you can hunt down threats in real-time. Not a drop of sweat needed.. read more  

Automated Kubernetes Threat Detection with Tetragon and Azure Sentinel
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Under the hood: Amazon EKS ultra scale clusters

Amazon EKScranked up its gear—you can now spin up clusters with a staggering100,000 nodesat your beck and call. That’s a cozy home for either1.6 million AWS Trainium chipsor800,000 NVIDIA GPUs. Welcome to the playground for ultra-scale AI/ML. Performance soars skyward by ditching old etcd consensus .. read more  

Under the hood: Amazon EKS ultra scale clusters
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6 Design Principles for Edge Computing Systems

Edge systemseach have their eccentricities, needing solutions as unique as they are:Chick-fil-Aswears byKubernetesto herd its standard operations. TheAir Force, however, prizes nimbleness and ironclad security for deployments scattered across the globe. Smart edge management? It’s a mix ofInfrastruc.. read more  

6 Design Principles for Edge Computing Systems
Vertex AI is Google Cloud’s end-to-end machine learning and generative AI platform, designed to help teams build, deploy, and operate AI systems reliably at scale. It unifies data preparation, model training, evaluation, deployment, and monitoring into a single managed environment, reducing operational complexity while supporting advanced AI workloads.

Vertex AI supports both custom models and foundation models, including Google’s Gemini model family. It enables organizations to fine-tune models, run large-scale inference, orchestrate agentic workflows, and integrate AI into production systems with strong security, governance, and observability controls.

The platform includes tools for AutoML, custom training with TensorFlow and PyTorch, managed pipelines, feature stores, vector search, and online and batch prediction. For generative AI use cases, Vertex AI provides APIs for text, image, code, multimodal generation, embeddings, and agent-based systems, including support for Model Context Protocol (MCP) integrations.

Built for enterprise environments, Vertex AI integrates deeply with Google Cloud services such as BigQuery, Cloud Storage, IAM, and VPC, enabling secure data access and compliance. It is widely used across industries like finance, healthcare, retail, and science for applications ranging from recommendation systems and forecasting to autonomous research agents and AI-powered products.