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The latest News and Information on Containers, Kubernetes, Docker and related technologies.

Deploy on Friday, Ep. 139

It's Friday, which means it's time to deploy! This week we're covering two weeks of news. On the Octopus side, we have new videos on vibe deployments and proving ROI with the Value Metrics Dashboard, a new Kubernetes migration webinar, and more! In the wider ecosystem, Kubernetes 1.36 "Haru" shipped with user namespaces going GA and Ingress-NGINX officially retired. Docker launched microVM sandboxes for AI coding agents. And Google said developer loyalty to AI tools is at zero.

7 best AI deployment platforms for production Kubernetes workloads in 2026

Training a model in a notebook is easy. What breaks teams is the step after, serving it reliably without haemorrhaging cloud budget or burying your SREs in YAML. The common trap: picking a platform that handles the model but not the surrounding stack. An AI deployment platform should orchestrate the full application graph (inference endpoints, vector databases, caching layers, and frontends) inside a single VPC, with GPU autoscaling that doesn't require a dedicated platform engineer to babysit.

#056 - Cloud Contradictions and Cautionary Tales with Corey Quinn (The Duckbill Group)

In this episode of the Kubernetes for Humans podcast, Itiel sits down with the internet's favorite cloud contrarian, Corey Quinn of the Duckbill Group. Corey shares his unconventional career path as a "cautionary tale," explaining why his knack for fixing horrifying AWS bills makes him a terrible employee, and why he absolutely refuses to touch Kubernetes in production.

VM Migration to Kubernetes: What Breaks and How to Prevent It

Here is what nobody putting together the business case for a VM migration to Kubernetes will tell you upfront: the compute is the easy part. Moving workloads off vSphere and onto Kubernetes is conceptually straightforward. The tooling has matured. The architecture is proven. Compute moves, storage remaps, and the platform team has a plan. The network is where projects quietly stall.

Inclusive AI vs. centralized AI: Can India avoid big tech concentration?

At the 2026 India AI Impact Summit in February 2026, 92 countries and international organizations (including the US, China, and the UK) signed a preliminary agreement that positions AI as both a development tool and a shared global responsibility. “India will not be a mere consumer in the AI age. We will be the creators, the builders, and the exporters of intelligence and we are proud to be able to participate in that future.” Gautam Adani, chairman of the Adani Group.

15: Optimizing AI Workloads: Balancing Cost, Performance, and Scalability with Bijit Ghosh

In this episode, Andrew Hillier and Bijit Ghosh discuss the evolving landscape of AI, discussing the growing prominence of inference over training, hybrid cloud strategies, balancing cost with performance, and the orchestration of complex hardware environments. The conversation also touches on emerging concepts like AI factories, the challenges of sovereign cloud, and how enterprises are navigating data gravity and regulatory constraints. It's a deep dive into optimizing AI infrastructure, managing costs, and the disruptive changes that are transforming both technology and business outcomes.

The New Kubernetes Monitoring Experience in Splunk Observability Cloud

In this video, I walk through the three main pieces of the new Kubernetes monitoring experience in Splunk Observability Cloud: the Kubernetes overview page for monitoring the status and top issues across your environment, the Kubernetes Entities page for troubleshooting individual instances with correlated metrics, logs, events, and configuration, and the Workload Optimization view for getting actionable recommendations on your CPU and memory resource allocation.

Dr. Argo Called - Do your custom resources need a check-up | Argo Unpacked Ep. #26

In this episode of Dr. Argo Called, we examine the GitOps “manifest dilemma” by comparing Helm, Kro, Kustomize, and Crossplane for Kubernetes deployments, and explore what their differences mean in a GitOps-driven workflow. We also delve into the often-overlooked topic of custom resource health checks in Argo CD—why they matter, why they shouldn’t reside within Argo CD, and how they could be designed more effectively.