Operations | Monitoring | ITSM | DevOps | Cloud

We are delighted to share a new initiative – the Ubuntu Appliance portfolio. Together with NextCloud, Mosquitto, Plex, OpenHAB and AdGuard, we have created a new class of Ubuntu derivatives: specialised appliance images that do one thing beautifully. Ubuntu Appliances transform a Raspberry Pi or PC into a secure, self-updating solution, free of charge. The Ubuntu Appliance mission is to enable secure, self-healing, single-purpose devices.

For IT Ops people working in organizations with a truly global presence – say managed service providers, banking, finance, aviation and utilities, coherently maintaining full visibility of service health and identifying service-affecting issues can be a big headache. As a global enterprise expands and grows its operations, be it through acquisition, or during the onboarding of new customers, there’s a tendency for multiple incident management and monitoring tools to accumulate.

In part one of this series, I introduced you to Kubeflow, a machine learning platform for teams that need to build machine learning pipelines. In this section, we will learn how to take an existing machine learning project and turn it into a Kubeflow machine learning pipeline, which in turn can be deployed onto Kubernetes. As you are going through this exercise, think about how you can convert your existing machine learning projects into a Kubeflow one.

There are many challenges that engineering teams face when attempting to incorporate a multi-cloud approach into their infrastructure goals. Kubernetes does a good job of addressing some of these issues, but managing the communication of clusters that span multiple cloud providers in multiple regions can become a daunting task for teams. Often this requires complex VPNs and special firewall rules to multi-cloud cluster communication.

The blessing and curse of cloud computing is flexibility. Now more than ever, companies leveraging the cloud are being revered for their ability to scale up usage of critical applications remotely for employees working from home, while in the same breath lambasted as IT costs sky-rocket. And in the midst of soaring usage, capacity, and cost come budget-cuts to fend off any other necessary evils during this challenging time.

Hello and welcome to another issue of This Month in Qovery! Qovery helps developer to deploy their applications in the Cloud in just a few seconds. This is a monthly summary of its progress and community.