Operations | Monitoring | ITSM | DevOps | Cloud

Azure Virtual Machines (VMs) represent the power of cloud computing, making the infrastructure of the digital age accessible to every business. With Azure VMs, Microsoft has democratized access to virtualized environments, allowing businesses to focus on building their applications without worrying about the underlying hardware. Azure VMs are a part of Microsoft’s Azure platform, one of the most comprehensive cloud platforms on the market.

GitLab is a collaborative Git repository that fosters open-source development by offering both free open and private repositories. With its extensive features such as issue tracking and wikis, GitLab empowers teams to collaborate effectively and create exceptional software solutions. GitHub and Bitbucket are similar tools. CircleCI has launched support for Gitlab, so it is helpful to learn to use it. In this tutorial, I’ll show you how to push a project to GitLab.

This article explores the rise of micro apps and microservices, driving faster and lighter software experiences. It discusses monitoring challenges in distributed systems and provides an introduction to monitoring Kubernetes. Learn about the basics, benefits, and differences of micro apps and microservices, along with insights into effective monitoring strategies. Discover MetricFire's monitoring solutions through a free trial.

If you’re still unpacking the latest terminology from Gartner’s 2023 AIOps market update, you aren’t alone. Subject matter experts from Moogsoft recently joined thought leaders from TIAA and Windward Consulting for a debrief on the panel interview Accelerating Your AIOps Journey Webinar. Almost half of technology leaders looking to improve productivity and fuel greater collaboration are struggling to explain AIOps use cases, benefits, and value to other business leaders.

Note: This post is co-authored by Simon Aronsson, Senior Engineering Manager for Canonical Observability Stack. AI/ML is moving beyond the experimentation phase. This involves a shift in the way of operating because productising AI involves many sophisticated processes. Machine learning operations (MLOps) is a new practice that ensures ML workflow automation in a scalable and efficient manner. But how do you make MLOps observable?