AIOps and Machine Learning: The perfect combination for observability

Tech pros rely on traditional monitoring to determine whether their systems are operating correctly through the aggregation and displaying of data. However, to convey this essential information, an abundance of alerts are generated, translating to a constant stream of noise. While some of these are vital towards addressing a specific problem, the majority are not.

Sifting through the ‘not’ is a process that can become a job all on its own. Complex IT infrastructures have microservice architectures that allow tech pros to observe, monitor, and analyse their organisations’ cloud environments efficiently. Being able to receive the alerts without the noise (and the resulting alert fatigue) is vital for enabling them to find a signal.

Integrating SolarWinds® Hybrid Cloud Observability allows tech pros to filter seasonal trends using time-series analysis so the end-user can receive guidance on exactly where action is required. Firms can then gain end-to-end oversight of their service delivery and component dependencies. Hybrid Cloud Observability brings control into multi-cloud environments because it integrates with AIOps (AI for IT operations) and machine learning (ML).

How can AIOps and ML be best leveraged?

The embedded AIOps and ML within observability can analyse the noise and the deluge of accumulated data so tech pros can move effectively to manage services that support customers and employees. In short: Observability transforms hours of analysis into seconds.

Don’t Replace, Improve

Observability will not replace traditional monitoring. Instead, observability uses the information gathered through monitoring as a critical element to its services.

Traditional monitoring uses metrics-oriented dashboards to assess telemetry data against basic, statistically relevant thresholds. It’s focused on a network, cloud, infrastructure, or app element so tech pros can identify anomalies, investigate problems, and discover a solution.

But monitoring has its limits. It doesn’t offer cross-domain correlation, service delivery insight, operational dependencies, or predictability. To make things worse, monitoring silos will inevitably develop over time. This is where observability can help. Observability analyses the collected data and compares it to expected outcomes and objectives. With this data, tech pros are better equipped to understand the state of their infrastructure and applications.

Proactive Versus Reactive

AIOps and ML allow observability to go further by delivering predictive analytics. An observability solution will detect a potential problem proactively, then automatically respond to it independently. However, when a tech pro needs to be involved, they can be alerted.

Embedded AIOps and ML will provide the necessary insights, automated analytics, and actionable intelligence through cross-domain data correlation, massive metrics, logs, and trace data. This is where you spot the signal, making the end solution easier to find.

Proactivity is maximised across Observation through the reduction of operational noise, allowing tech pros —including DevOps and security teams—to discover anomalies when they should be—before the disruption has occurred. Having shifted away from a reactive stance to a proactive one, teams can then better automate tasks and advance closed-loop operational management, reporting, and capacity planning efficiencies—across IT domains.

Observability advances business agility by allowing tech pros to identify problems and deficiencies. They then characterise and predict impactful business service, component, and activity-state changes. Integrated observability is scalable, has lower administrative overhead, optimises IT efficiency, eliminates redundant tools, and helps reduce costs.

Observability allows teams to visualise and continuously analyse business service and component relationships, deviations, and dependencies. As a result, they also see improvements in performance, compliance, and resilience.

A Next Generation Solution

With hybrid remote work here to stay and with the rise of SaaS apps and ubiquitous smart devices—the loss of connectivity can lead to poor workplace communication or large-scale disruption. AIOps and ML help observability provide protection against these difficulties.

But don’t think of observability as one more “thing” or another technology thrown into the stack. Instead, it’s a next-generation, integrated IT infrastructure, application, and database performance management solution. Like butter, observability makes everything better.

Observability, with integrated AIOps and ML, enables firms to holistically manage IT service delivery with ease. By promoting continuous improvements in performance and reliability, observability is a worthwhile, cost-effective option for IT organisations of any size.

Observability enhances CX across complex, diverse, distributed hybrid and cloud environments and will undoubtedly take traditional monitoring practices to the next level.

Thomas LaRock is the Head Geek™ at SolarWinds.