Operations | Monitoring | ITSM | DevOps | Cloud

Automation has become more than just a “nice to have” choice. It’s an essential part of the modern business landscape, promising increased efficiency, reduced costs, and improved accuracy. However, despite its potential benefits, many organizations struggle when trying to implement automation. In this article, we’ll explore some of the most common implementation mistakes we’ve encountered and how to navigate them effectively.

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Even the most reliable and well-designed software systems experience failures. Tracking incident response metrics helps teams strengthen both organizational preparedness and system resilience by uncovering trends, gaps, and opportunities for improvement. In short, important metrics for incident management are: Understanding these metrics helps engineering leaders improve service uptime, meet SLAs, and align operational capacity.

For a lot of teams, incident management can be a bit of a headache. It's stressful. It's not optimized. The whole process can feel like it's being held together with tape. Worst of all? Responders are the ones feeling the brunt of it. But in reality, your customers are, too. Think about it: But honestly, the situation doesn't even have to be so dire. Things can be, generally speaking, totally fine.

We are thrilled to share the exciting news that Qovery has once again been recognized as a Momentum Leader for DevOps in the Winter 2024 Grid Report. This marks the third consecutive time we've received this prestigious acknowledgment, and we couldn't be more grateful for the ongoing support of our incredible community of users 🙏

Efficient code can be defined by numerous factors, in addition to what others deem necessary based on their experiences. Per this article, we will define it as: Measuring and comparing your code on its design, optimization, and performance; while considering its use cases, compute resources, quality, scalability, and structure. Along with our definition, it is important to understand some key concepts before we dive further into some best practices and additional support to contextualize our definition.

Edge AI is transforming the way that devices interact with data centres, challenging organisations to stay up to speed with the latest innovations. From AI-powered healthcare instruments to autonomous vehicles, there are plenty of use cases that benefit from artificial intelligence on edge computing. This blog will dive into the topic, capturing key considerations when starting an edge AI project, main benefits, challenges and how open source fits into the picture.