The latest Atlassian outage goes to show that every cloud provider is prone to unplanned downtime sooner or later. While every company strives to achieve that unicorn status of zero downtime, it is almost impossible to achieve that in the face of “Unknown Unknowns.” I analyze it and offer some solutions on how to mitigate that if disaster strikes you.
In digital economy, you must move fast to survive. Not in six-month release cycles. But moving with fast release cycles, continuous releases, a mature CI/CD pipeline is only a portion of the solution. If you continue to break your systems at a faster rate but are unable to fix them faster as well, you are setting up for unplanned disasters that will hurt your business sooner than later. I discuss some of the fixes in this blog.
Most of the AIOps companies are doing the process right, some use AI and ML properly, but most fail on how to automate data processing, or DataOps, on how to get the right data to AIOps tools at the right time. In this eBook "Data Done Right for AIOps," I discuss this in detail and offer some possible solutions including Robotic Data Automation (RDA).
What are the criteria for selecting a good AIOps solution? How do you compare and measure the solutions one against another? Especially when there are so many solutions out there all claiming to solve the problem better than the others! In this article, I outline the top 5 criteria that all buyers should keep in mind when considering an AIOps solution. Let me know if you have more.
The network is the foundation for all applications. With the increase in distributed applications and their hybrid nature, the network has become even more important. Delegating more and more tickets to AI will not only help reduce pressure on support team resources, but also fundamentally shift operations from being focused on reactive troubleshooting to proactive remediation.
The shine on AI and ML projects has never been brighter. The C-suite is excited about the prospects of these projects and is throwing money at them. Why then, with all this enthusiasm, can’t AI/ML projects gain more traction across the business? Why are so many of them failing? The short answer: your business executives are not getting timely insights or sometimes even the right insights.
I am very honored to be part of the Edgevana podcast series talking to the legendary Mark Thiele on various edge, AI, AIOps, total observability at edge, and other related topics.
Modern complex systems are easy to develop and deploy but extremely difficult to observe. Their IT Ops data gets very messy. If you have ever worked with modern Ops teams, you will know this. There are multiple issues with data, from collection to processing to storage to getting proper insights at the right time.
Summary I did a deep dive vendor research report on Zebrium which specializes in automatic root cause analysis using machine leaning. Quick summary from the report: Zebrium is an Observability/AIOps platform that uses unsupervised machine learning to auto-detect software problems and automatically find root causes, reducing manual labor and speeding […]
I was having a conversation with a CxO level customer as part of an AIOps/Observability workshop, and from what I could tell, most are confused about how to properly operationalize cloud-native production environments – especially the monitoring/observability portion. Here is how the conversation went.