Under the hood, Moogsoft Observability Cloud extends AI-based intelligence so that it starts with raw observability data analysis. It discovers your infrastructure services to collect and analyze the time-series metrics locally, along with turning time-series metrics and event data from your existing tools into actionable insights.

With more and more people working from home and the ever-increasing complexity of IT infrastructure, it’s important to understand the best way to leverage Machine Learning (ML) and Artificial Intelligence (AI) to improve IT operations. ML and AI have promised to bring disruptive changes to IT operations, and many organizations have already decided to adopt Artificial Intelligence for IT Operations (AIOps) or to do it soon. Yet, implementing and deploying AIOps is still very challenging.

Leading analysts continue to acknowledge BigPanda’s leading role in the AIOps ecosystem. Earlier this year the GigaOm AIOps Radar report placed BigPanda in the leader section for the company’s strong market impact as a platform that delivers event correlation at scale.

Technology trends transform human behavior permanently. In the past decade, we have improved as a society by embracing digital lives that drive faster collaboration and automation and save us a significant amount of time. The IT Operations landscape is not any different, and artificial intelligence (AI) is at the forefront of that.

AIOps is one of the fastest-growing categories in the IT operations management (ITOM) software space. With increasing demands on IT, the ability to leverage AI is critical to successful operations. The adoption statistics are quite staggering. Gartner reports that by 2022, 40% of large enterprises will use AIOps tools to support or replace monitoring and service desk tasks.

Artificial intelligence for IT Operations (AIOps) still sounds like something from the future to a lot of IT professionals. Maybe you’ve heard about the benefits but don’t think your organization is ready. In these three short, informative videos, Kia Behnia, Vice President of IT Operations, addresses three key questions IT pros still have when it comes to AIOps.

AIOps adoption is on the rise. According to Gartner, by 2023 40 percent of DevOps teams will augment application and infrastructure monitoring tools with AIOps platform capabilities. Use cases are also expanding beyond IT to include IT Service Management (ITSM), digital experience monitoring (DEM), DevOps, Application Performance Monitoring (APM) and third party services.

Legacy monitoring tools fall short for SRE teams and DevOps pros tasked with maintaining uptime of key applications in modern, cloud-based IT systems. To have visibility and control over these environments, these teams must collect and analyze more granular, underlying system information — observability data. This article explains why the only way for SRE teams and DevOps pros to extract the necessary insights from this data is through the application of AI capabilities.