With Azure Sphere, customers can connect legacy, brownfield devices to the cloud securely through AT&T’s cellular network.
With tight integration with GKE and Kubeflow, the service has evolved into an end-to-end platform that supports data preparation, transformation, training, model management, deployment, and inference.
Compared to other hybrid and multi-cloud services available in the market, Azure Arc is unique. It addresses the scenarios involving both legacy virtual machines and modern Kubernetes clusters.
In the previous part of this series, I introduced the core building blocks of cloud native edge computing stack: K3s, Project Calico, and Portworx.
This tutorial will walk you through the steps involved in installing and configuring this software on an edge cluster, a set of Intel NUC mini PCs running Ubuntu 18.04. This infrastructure can be used for running reliable, scalable, and secure AI and IoT workloads at the edge.
Customizing K3s Installation for Calico
By default, K3s will run with flannel as the Container Networking Interface (CNI), using VXLAN as the default backend. We will replace that with a CNI-compliant Calico.
Read the entire article at The New Stack
Janakiram MSV is an analyst, advisor, and architect. Follow him on Twitter, Facebook and LinkedIn.
After Kubernetes, the service mesh technology has become the most critical component of the cloud native stack. Platform vendors and cloud providers are now shifting their focus to service mesh to offer developers and operators a differentiated experience. Here is an analysis of the current state of service mesh.
Kubernetes is fast becoming the preferred infrastructure for edge computing. The promise of agility, scale, and security is getting extended to the edge infrastructure. K3s, Portworx, and Calico form the core of the modern, cloud native edge computing stack.
With Cloudflare becoming a customer of Vapor IO, Cloudflare Workers serverless computing platform will become available in every micro datacenter powered by Kinetic Edge.
With the integration of BERT with ONNX, developers can train a model, exporting it to ONNX format and using it for inferencing across multiple hardware platforms.