At Grafana Labs, we’re all big fans of the Prometheus and Grafana combination. To an extent, we just won’t shut up about it. We strongly believe in simplicity and think you shouldn’t need any extra effort to understand the metrics of your service(s) holistically. Yet we’ve all been in that situation where it is challenging to fully grasp what the metrics of our service(s) are and what they do. While naming conventions exist, at times they are not followed or enforced.

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.

Today, monolithic applications evolve to be too large to deal with as all the functionalities are placed in a single unit. Many enterprises are tasked with breaking them down into microservices architecture. At LogicMonitor we have a few legacy monolithic services. As business rapidly grew we had to scale up these services, as scaleout was not an option.

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.

In this video, we are going to take a look at what memory bloat is, and how you can use Scout to eliminate it from your applications. Memory related performance issues have the potential to bring your entire application down, and yet, most APMs completely ignore this fact and fail to provide any useful way of monitoring memory usage at all.

Many Mattermost DevOps teams work in “Microsoft Shops”—organizations that use Office 365 apps with the Microsoft 365 business plan—and want to tightly integrate with Microsoft tools to more easily collaborate with the rest of the company. Mattermost E20 was architected from the ground up to be highly flexible. As such, integrating with other platforms including Microsoft is easy. Here’s how.

The SolarWinds® Pingdom® Website Speed Test is one of our most popular tools. It helps countless users understand how their websites are performing and what they can do to improve their site speed. The Website Speed Test can test websites from locations around the globe, and it produces many key results to give you in-depth insight into how quickly your webpages are loading.

Most of the IT industry is floating on the cloud these days. From hosting websites to storing data, organizations have started to use the cloud for various purposes, so why not for log monitoring? In this blog, I’ll explain how cloud-based log monitoring is beneficial for you and how you can use SolarWinds® Papertrail™ for it.

It is general knowledge that most businesses are used to managing their IT assets through only a spreadsheet and a list of invoices. The critical problem with this system is that it is far too simple a mechanism to be able to maintain the asset inventory for hundreds or thousands of hardware devices and software licenses, let alone handle the different aspects of the IT asset life cycle like.

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.