You’ve probably seen the term AIOps appear as the subject of an article or talk recently, and there’s a reason. AIOps is merging DevOps principles with Artificial Intelligence, Big Data, and Machine Learning. It provides visibility into performance and system data on a massive scale, automating IT operations through multi-layered platforms while delivering real-time analytics.
I often have discussions with N-able partners who need help capturing data for either regular reporting or just to have when a customer asks for it. Often people will use a tool like BrightGauge to pull data from their RMM platform and generate dashboards and reports. However, these can be overly complex if you only need to capture the data so you have it and can act upon it. What I regularly recommend is that you create a monitoring script to capture just the data that is needed.
For many years, system uptime was the primary measure of reliability, especially when the most popular method of running software was on bare metal, on-premises servers. If a server was shutdown, rebooted, or otherwise became unavailable, downtime was expected until a system administrator could manually restart it.
After the global pandemic and lockdowns, most businesses look onward to online solutions for their applications. They want to take their business online by creating mobile or web applications. Companies are looking to monitor every aspect of the application, including deployment, bugs, API failures, etc. But the most important thing is how the application behaves when it goes into the hands of the end-users.
