Operations | Monitoring | ITSM | DevOps | Cloud

Rancher is a Kubernetes management platform that creates a consistent environment for multicloud container operation. It solves several of the challenges around multicloud Kubernetes deployments, such as poor visibility into where workloads are running and the lack of centralized authentication and access control. Multicloud improves resiliency by letting you distribute applications across providers.

In this post we want to give a simple introduction for using network policies in a sample project and explaining how it works in K3s to help improving the security of your deployments. There is a common misunderstanding about K3s support for network policies, as K3s is using flannel CNI by default, and the Flannel CNI doesn’t support network polices.

Preview environments are temporary environments where developers can test code changes before deploying them to production, also called ephemeral environments, they’re temporary and should be discarded after testing changes. Carrying out tests using accurate data is a major challenge when creating and destroying environments. Put differently, you need realistic data and traffic in the preview environment to reflect the performance of code changes in production.

Canonical is happy to announce that Charmed Kubeflow 1.7 is now available in Beta. Kubeflow is a foundational part of the MLOps ecosystem that has been evolving over the years. With Charmed Kubeflow 1.7, users benefit from the ability to run serverless workloads and perform model inference regardless of the machine learning framework they use.

AirDroid Remote Support for Business is an all-in-one remote assistance solution for IT professionals and customer service teams to tackle both hardware & software issues.

Artificial Intelligence (AI) has become a buzzword in recent years. From chatbots to self-driving cars, AI has transformed how we live, work, and interact with the world around us. AI technology has been deployed across various sectors, including healthcare, finance, manufacturing, and more, to improve efficiency, accuracy, and decision-making capabilities. However, as AI systems become more complex, monitoring them to ensure optimal performance and prevent issues or errors becomes crucial.