DevOps, site reliability engineers (SREs), and network operation center (NOC) teams have a responsibility to ensure infrastructure reliability. This is best managed with the right tools for incident response. This field infuses advanced technologies like artificial intelligence and machine learning to create new classifications like AIOps (Artificial Intelligence for IT Operations). AIOps helps teams identify errors and repair them faster. SREs (site reliability engineers) leverage these assets in a variety of AIOps use cases, including incident detection, noise reduction, issue resolution, continuous improvement initiatives, and integrations.
To understand when to use AIOps, we’re breaking down what it is and why it matters, and exploring some practical use cases.
Why is AIOps in demand?
Current software development focuses on rapid deployments. This constant iteration puts a strain on infrastructure reliability, SREs focus on reliability specifically and strive to accelerate incident response to ensure uptime and that service level objectives (SLOs) are met. They need better ways to monitor and react to issues. With ever-expanding estates, they don’t have the time to manually analyze all the telemetry data to find anomalies.
Playing investigator slows their response time and impacts reliability. If they can’t meet SLOs, their entire enterprise is impacted. AIOps gives them the support they need by proactively detecting anomalies in their environments before they have time to snowball into bigger issues.
AIOps also fully supports the philosophy of DevOps because it breaks down data silos. An AIOps platform works best when there are large amounts of data, which can include observational and engagement data, as well as data from third-party tools. A variety of algorithms and machine learning is then applied across the data to find tailored insights that can help teams more quickly identify, diagnose, and resolve issues. The end goal is to help your teams work faster and more efficiently using the approach that works best. Spoiler: sometimes it isn’t the most complex machine learning algorithm.
AIOps use cases
How can you use these new tools? What impact will they have on your incident management workflows? There are five specific use cases where you can apply AIOps.
1. Incident detection
Adding an AIOps solution expands your toolkit so that you’ll be able to detect problems much sooner. For example, the detection of anomalies can help you keep issues from happening before they start. It’s the most proactive approach to ensure you are aware of the issue before there is any effect on your customers.
2. Noise reduction
Alert fatigue is a huge problem in incident response. A barrage of alerts means you can become numb to all alerts, even if they’re critical. Ideally, you want to suppress low priority alerts and group alerts that are related. With an AIOps solution, you can correlate, suppress, and prioritize alerts. Thus, your team can focus on issues that are the most critical to reliability.
Incidents can be complicated and having a guide to point you in the right direction helps. An AIOps solution automatically analyzes the data for a holistic understanding of the incident. It then provides insight and the context you need to resolve it.
4. Continuous improvement
Past experiences, current usage, and user feedback provide excellent data to help prevent issues that are similar to historic issues, which is crucial for continuous improvement. AIOps trains on this knowledge to continually get smarter and deliver tailored correlations, insights, and recommendations.
5. Data integration
Incident data from any source integrates with your current incident management tools and workflows. The more data you have coming in, the better trained your machine learning will be and the more tailored and useful your results will be. An AIOps solution ingests data, enriches it with context, and sends notifications to the relevant teams or responders in the incident management tools teams are already using. This way, teams don’t waste critical time switching between tools.
Why New Relic Applied Intelligence is unique
New Relic Applied Intelligence uses raw monitoring data to power machine learning and can ingest, correlate, and alert on sources outside of New Relic, such as PagerDuty, and more. In this way, users remove silos and get a better big picture of the issue that is occurring with important context around it. And integration with existing incident management tools means that New Relic Applied Intelligence meets teams where they’re already working.
This provides users with:
- Fastest time-to-noise-reduction: New Relic gives customers the fastest time-to-noise-reduction, using automatic, out-of-the-box correlations that continually self-improve. New Relic automatically reduces noise by enriching incidents with context in real time, includes AI/ML-powered suggested correlations, and frees you from steep learning curves, lengthy implementation and training times, or complex integrations typically found with other AIOps tools.
- Fits existing incident management workflows: New Relic is the only AIOps solution that meets you where you are, delivered within your existing incident management workflows and tools. Simply connect your existing tools as data sources and destinations via New Relic’s guided configuration UI, and the solution takes care of the rest, ingesting incident data from your toolchain, enriching it with context, providing smart suggestions and guidance, and delivering relevant insights about incidents to PagerDuty, Slack, Jira, ServiceNow, VictorOps, and your tools of choice.
- Faster time-to-value: Analyze and take action on more data types. While tools like PagerDuty provide alerting and escalations based on alerts, New Relic allows you to ingest, analyze, and take action on multiple data types, including alerts, logs, metrics, deployment events and more. This gives you better context into incidents that occur and how they impact your broader environment, so you can diagnose and prioritize problems faster.
- Transparency: Knowing why incidents are correlated builds trust. AIOps tools shouldn’t be a black box. New Relic Applied Intelligence provides full transparency into why and how correlations are performed and lets you to tune the system with your own correlation logic (by simply telling New Relic what data to compare and how you want to correlate the data), so you have confidence in the ML models that reduce noise while ensuring that critical signals don’t go unnoticed.
Explore New Relic Applied Intelligence
These AIOps use cases demonstrate how your organization can benefit from adopting an AIOps solution. With proactive anomaly detection, alert noise reduction, issue enrichment, and smarter notification, New Relic Applied Intelligence gets you closer to root cause and enables you to detect, respond to, and resolve issues faster than ever before.
If you’re ready to learn more about preparing for AIOps, don’t miss our ebook, How to Prepare for AIOps: Four steps for a successful deployment.
The views expressed on this blog are those of the author and do not necessarily reflect the views of New Relic. Any solutions offered by the author are environment-specific and not part of the commercial solutions or support offered by New Relic. Please join us exclusively at the Explorers Hub (discuss.newrelic.com) for questions and support related to this blog post. This blog may contain links to content on third-party sites. By providing such links, New Relic does not adopt, guarantee, approve or endorse the information, views or products available on such sites.