From cloud to edge, hardware-agnostic AI/ML In January Canonical will reconfirm its presence at CES 2025; here we will be showing a cutting-edge AI/ML demo that showcases how AI models can be trained, deployed, and updated seamlessly across various hardware platforms.

There have been significant improvements in Tracealyzer over the last years. If you haven’t tried it in a while—or if you’re just getting started—here are some tips and tricks that can be handy when analyzing your FreeRTOS applications. As you may know, the TraceRecorder library automatically records task scheduling and FreeRTOS API calls using the standard trace hooks in the FreeRTOS kernel.

SLA vs SLO vs SLI, what’s the difference anyway? Workplace success relies on clear expectations to help leaders and employees thrive together. As such, the partnership between customer and provider requires the same clarity to maintain service satisfaction. This is why Service Level Agreements (SLAs), Service Level Objectives (SLOs), and Service Level Indicators (SLIs) exist in the first place.

Microsoft Teams is deeply embedded in many organizations’ workflows, acting as a hub to both communicate and collect information about issues and ongoing projects. However, as with most communication platforms, it can be challenging to context-switch between conversations, tickets, and monitoring data when troubleshooting collaboratively.

To ensure that your organization’s cloud spend is efficient, you need detailed and granular visibility to understand what comprises your costs, what causes them to change, and how the cloud services and resources you use are enabling your business goals. Extending your visibility and more closely monitoring your cloud costs can position you to successfully adopt FinOps, which provides a framework that can help you maximize the value you get from your cloud spend.

It’s a story many network administrators dread: leaving the office on a Friday afternoon with everything running smoothly, only to return Monday morning to a nightmare of system-wide failure. If you’re a network administrator, you know the quiet comfort in logging off on Friday, satisfied that all servers are operational, backups are complete and the network is running efficiently. It’s the moment when you finally let out a sigh of relief, looking forward to a stress-free weekend.

For today’s network operations teams, change is a constant. Applications, app delivery chains, software-defined and physical infrastructures, cloud services, and more are in continuous flux. Further, as organizations continue to pursue ever more strategic digital transformation efforts, the pace of this change only accelerates. These days, about the only constant is the demand being placed on network operations teams.

I think we can all agree that, in general, different users have different needs. For instance, we’ve found that developers generally use Artifactory to find, select, and then install packages into their development environment, while administrators tend to use Artifactory for troubleshooting, confirming package operations, and other related analyses.

Handling IT alert data can feel like you’re drowning in information. The average BigPanda customer uses more than 20 observability and monitoring tools. Between system logs and user reports, an overwhelming amount of information is coming from all directions. That’s why normalizing data is such a critical part of IT operations. Data normalization in IT incident management involves putting data from various tools into a standard format.

In the modern, technology-driven world, IT Management serves as the core framework that empowers organizations to manage and optimize their technology resources effectively. From overseeing daily operations to planning for future growth, IT Management is essential in structuring, organizing, and aligning IT functions with business goals. By strategically managing the digital infrastructure, IT Management ensures that organizations remain agile, efficient, and competitive.