In an era where digital presence is synonymous with business continuity, unexpected downtime haunts every IT department across industry domains. The quest for operational perfection pivots around not just maintaining uptime but proactively ensuring it. Artificial Intelligence for IT Operations – a ray of hope in this persistent pursuit. Still, the question remains: Is achieving zero unexpected downtime with AIOps a tangible reality?

The increasing rise of Artificial Intelligence for IT Operations (AIOps) in information technology (IT) is rapidly emerging as a transforming force that will redefine the operational paradigms. Essentially, AIOps fuses machine learning, big data analytics, and various IT tools to automate and improve IT Operation processes, including event correlation, anomaly detection, and event causality.

In the world of digital banking, maintaining a seamless and efficient online experience is paramount. However, even the most robust systems can encounter issues that disrupt service and degrade performance. Let us delve into a recent incident that impacted eBanking services of one of our customers, highlighting the criticality of database management and the steps taken to resolve the issue.

In digital transactions, even the slightest hiccup can ripple through the system, causing significant disruptions. Our recent encounter with an unexpected system slowdown and a noticeable drop in transaction success rates is a testament to the intricate balance required to maintain seamless operations. This post aims to shed light on the incident, our findings, and the measures we’ve taken to fortify our system against future disturbances.

In the dynamic environment of core banking systems, maintaining seamless operations is crucial. However, unforeseen complications can arise, leading to critical incidents that demand immediate and effective resolution. A recent incident involving an application patch malfunction presents a compelling study on the intricacies of managing and resolving system anomalies in real-time.

In server management, high memory utilization is more than just a metric; it’s like a lighthouse signaling potential performance degradation, service disruption, and, in severe cases, complete system downtimes. Here we delve into a recent incident involving an App Server for one of our customers, which underscores the criticality of proactive monitoring, swift incident response, and strategic problem resolution.

Service incidents are unavoidable in today’s complex and dynamic IT environments. They can cause significant disruption to business operations, customer satisfaction, and revenue. However, many organizations are still struggling to manage service incidents effectively. Here, we will explore some of the common challenges faced by ITOps team and how HEAL, an AI-powered tool, can help conquer them.

Your device pings, signaling another tech alert. Before you can address it, two more chime in. We all know the feeling. In today’s digital world, it’s easy to feel overwhelmed by the sheer number of notifications we receive. But what if there was a smarter way to handle them?

Machine Learning (ML) for Root Cause Analysis (RCA) is the state-of-the-art application of algorithms and statistical models to identify the underlying reasons for issues within a system or process. Rather than relying solely on human intervention or time-consuming manual investigations, ML automates and enhances the process of identifying the root cause.

There are many instances in our lives where we are stuck in issues and try to understand what caused them. Our initial thoughts are to identify the reason and the cause. We aim to trace the issue back to the origin and try to address them from where it all started. Just like, when we get common cold, we try to figure out where we contracted it. Was it the late-night smoothie or exposure to someone with COVID symptoms? We never know until we figure out.