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Linux

MicroK8s HA tech preview is now available

High availability (HA) for MicroK8s, the lightweight Kubernetes, is now available as a tech preview for Linux, Windows and macOS. The Kubernetes control plane can now be distributed across multiple nodes, bringing resiliency to the cluster while maintaining a low footprint using Dqlite, the distributed SQL engine as the Kubernetes datastore.

Encryption at rest with Ceph

Do you have a big data center? Do you have terabytes of confidential data stored in that data center? Are you worried that your data might be exposed to malicious attacks? One of the most prominent security features of storage solutions is encryption at rest. This blog will explain this in more detail and how it is implemented in Charmed Ceph, Canonical’s software-defined storage solution.

Data science workflows on Kubernetes with Kubeflow pipelines: Part 2

This blog series is part of the joint collaboration between Canonical and Manceps. Visit our AI consulting and delivery services page to know more. Kubeflow Pipelines are a great way to build portable, scalable machine learning workflows. It is a part of the Kubeflow project that aims to reduce the complexity and time involved with training and deploying machine learning models at scale. For more on Kubeflow, read our Kubernetes for data science: meet Kubeflow post.

Canonical, Elastic, and Google team up to prevent data corruption in Linux

At Elastic we are constantly innovating and releasing new features. As we release new features we are also working to make sure that they are tested, solid, and reliable — and sometimes we do find bugs or other issues. While testing a new feature we discovered a Linux kernel bug affecting SSD disks on certain Linux kernels. In this blog article we cover the story around the investigation and how it involved a great collaboration with two close partners, Google Cloud and Canonical.

Netdata Agent v1.23: Kubernetes monitoring & eBPF observability

Deploying and monitoring performance for an entire Kubernetes cluster can be complex. To simplify the process, we’ve added service discovery functionality to eliminate complex configuration, in addition to more advanced monitoring for viewing activity inside containers. Service discovery identifies k8s pods running on a cluster and immediately starts monitoring system performance. All containers are identified, regardless of complexity.

Data science workflows on Kubernetes with Kubeflow pipelines: Part 1

Kubeflow Pipelines are a great way to build portable, scalable machine learning workflows. It is one part of a larger Kubeflow ecosystem that aims to reduce the complexity and time involved with training and deploying machine learning models at scale. In this blog series, we demystify Kubeflow pipelines and showcase this method to produce reusable and reproducible data science.