Artificial Intelligence for IT Operations (or AIOps for short) continues to be a hot topic among developers, SREs, and DevOps professionals. The case for AIOps is especially crucial given the expansive nature of today’s observability efforts across hybrid and multi-cloud environments. As with most observability platforms, it all starts with your telemetry data: metrics, logs, traces, and events.

When you’re investing in automation solutions, ultimately, tangible results need to follow quickly. Getting a return on investment (ROI) out of an automation project after two years is something that would have been OK in the not-so-distant past but is no longer acceptable nowadays. With the current speed of change, where new technologies come and go and existing ones evolve at lightning speed, IT teams require much faster time to value on automation investments.

In the last half-decade, AIOps and observability have arguably been the hottest two topics in IT operations management. Gartner first mentioned AIOps—Artificial Intelligence for IT Operations—in 2016, defining it as using big data and machine learning to automate IT operations processes, such as event correlation, anomaly detection and causality determination.

Artificial intelligence (AI) and associated technologies, such as machine learning and natural language processing (NLP), are used for daily IT operations tasks and activities. AIOps supports IT Ops, DevOps, and SRE teams working smarter and faster to identify digital-service issues earlier and address them quickly, preventing disruptions to business operations and customers. This is accomplished through algorithmic analysis of IT data and Observability telemetry.