SAN FRANCISCO: New Relic Inc., the industry’s largest and most comprehensive cloud-based observability platform built to help customers create more perfect software, enhanced New Relic AI, a suite of AIOps capabilities built for on-call DevOps, Site Reliability Engineering (SRE) and network operations center (NOC) teams responsible for operating modern infrastructure.
New Relic AI provides advanced applied intelligence (AI) and machine learning (ML) technologies to help customers detect, diagnose and resolve incidents faster, and continuously improve incident management workflow.
DevOps and SRE teams are under increased pressure to meet service level objectives, ship software without errors, and quickly fix incidents before their customers notice. More and more frequently, teams now find themselves bombarded with alerts sent from a mix of fragmented tools making it even more difficult to detect, diagnose, and resolve potential problems. New Relic AI was designed to provide on-call teams with intelligence and automation that augments their existing incident management teams and workflows to help get closer to root cause faster.
“New Relic’s goal is to help reduce the toil and anxiety of running modern systems for engineering teams. We’re proud to report that our early-access customers reported that they have seen automatic reductions in alert noise by 50 percent — and some as much as 80 percent within days,” said Guy Fighel, GVP and Product GM at New Relic. “New Relic AI is the only solution that has the automation, intelligence and scale-out architecture needed to deliver true observability and offer precise insights that today’s modern and complex enterprises require. We continue to push the boundaries to empower DevOps and SRE teams as we enhance our platform relentlessly.”
“New Relic AI’s proactive detection capability was very easy to set up and use. There were zero agent configuration changes or deployments needed,” said Jeffrey Hines, Senior Site Reliability Engineer, Signify Health. “Specifically, it helped my team achieve speed, agility and provided operational visibility which ultimately helps us reduce incidents, integrate machine learning and analytics into operations and improve overall customer experience.”
“Today, the biggest problem IT Ops teams struggle with the most is making sense of vast volumes of event alert noise, impacting a team’s ability to focus on building flawless software. With New Relic AI, our teams will have a clear understanding of how specific issues affect business services, allowing them to quickly identify and prioritize the most business-critical issues. With this launch, we look forward to harnessing the power of targeted intelligence and ultimately optimizing cost,” said Peter Hammond, Global Head of Technology Operations, Morningstar, Inc.
New Relic AI delivers a holistic AIOps solution that not only understands historical alerts, but also applies machine learning and AI to significantly reduce alert noise, enrich incidents with context, and provide intelligence and automation to on-call teams in real-time. Deeply integrated with the New Relic One observability platform, New Relic AI is an open incident correlation and intelligence solution that is source and data agnostic. With unique access to NRDB, a unified telemetry database, New Relic AI fuels ML models and provides an intelligent, context-rich incident response workflow, drawing on key capabilities that include:
Proactive Detection to detect problems earlier: Continuously evaluates telemetry data for anomalies and proactively notifies customers in their existing collaboration tools. This allows for quick action to prevent larger problems before they impact customer experience. New Relic AI enables customers to ingest, analyze, and take action on multiple data types, including alerts, logs, metrics, deployment events and more. This gives teams better context into incidents that occur and how they impact the broader environment, so they can diagnose and prioritize problems faster.
Incident Intelligence to reduce alert noise and diagnose and respond faster: New Relic AI deeply integrates with many data sources to group related alerts and incidents and includes AI/ML-powered suggested correlations to help customers prioritize alerts and focus on the most important issues. Alert noise is automatically reduced by correlating related alerts, events, and incidents, while also suppressing flapping and low-priority alerts. Correlated incidents are enriched with context, automatically classified based on golden signals (i.e. errors, saturation, traffic, latency), as well as identifying related components affected and suggesting responders, to help on-call teams get closer to root cause and take action faster. In addition, it frees users from the steep learning curves, lengthy implementations and complex integrations typically found with other AIOps tools. By leveraging incident correlation, early access customers have reported that they have seen automatic reductions in alert noise by 50 percent.
Deep integration with existing incident management workflows: New Relic AI integrates with Slack, PagerDuty, ServiceNow, OpsGenie, VictorOps and other tools to fit within customers’ existing incident management workflow. Enriched incidents with relevant context and ML-powered guidance and suggestions are automatically shared in team’s existing workflows, removing the need to switch between tools in times of crisis. Customers can see a live view of ingested data, an intelligent summary of each incident, and have the ability to tune correlations with user feedback.
According to Gartner, “AIOps will detect patterns a human would be unlikely to uncover, including those that reveal cause and effect. From this determination of causality, models should be created that will help decide which IT metrics should be mapped to which business objective. Observe these over time to refine each model; ensure that it is up-to-date and that any assumptions it makes remain accurate. Through its usage of machine learning algorithms, AIOps specifically offers a mathematical way to find the hidden connections, causes and opportunities in the data that make this process possible.”