AIOps-Driven Infrastructure Excellence for Global Event Operations

A global leader in live entertainment and ticketing, operating across more than 40 countries, faced CMDB performance bottlenecks and limited monitoring on its AWS infrastructure. Myridius deployed a multi-layered observability stack with AI-driven alert correlation, achieving 99.95 percent availability and a 50 percent reduction in mean time to resolution.

Key Outcomes

• 99.95 percent system availability.
• 50 percent reduction in mean time to resolution.
• Reduced alert fatigue through AI-driven correlation.

Overview

A global leader in live entertainment and ticketing, operating across more than 40 countries and powering tens of thousands of events annually, faced mounting pressure on its AWS-hosted corporate infrastructure. Its Configuration Management Database experienced performance bottlenecks and slow query execution, while limited monitoring made it difficult to detect anomalies, track changes, and ensure compliance, risking disruption to mission-critical operations serving millions of fans. Myridius deployed a multi-layered observability stack combining real-time monitoring, application performance management, and AI-driven incident correlation. As a result, the organization achieved 99.95 percent system availability, a 50 percent reduction in mean time to resolution, real-time observability, reduced alert fatigue, and monitoring that scales with seasonal event surges.

Client Context

The client is a global leader in live entertainment and ticketing, operating across more than 40 countries and powering tens of thousands of events annually for millions of fans, on AWS-hosted corporate infrastructure.

Reliable, observable infrastructure mattered here because CMDB bottlenecks and limited monitoring directly threatened the availability of mission-critical event and ticketing operations. What was at stake operationally was the organization's ability to serve fans without disruption, particularly during seasonal demand spikes when event volume and ticketing load peak.

The Challenge

The organization faced mounting pressure on its AWS-hosted corporate infrastructure. Its Configuration Management Database experienced performance bottlenecks, slow query execution, and difficulty handling infrastructure metadata at scale, while limited monitoring made it challenging to detect anomalies, track system changes, and ensure compliance.

Consider a peak event period. Infrastructure load surged, yet the team had limited real-time visibility into latency, error rates, and resource utilization, and a strained CMDB made it harder to understand the environment. Anomalies could go undetected until they affected operations, risking disruption to mission-critical event and ticketing services that serve millions of fans worldwide.

Transformation Goals

The engagement focused on north stars that connected infrastructure reliability to AIOps integration, operational efficiency, and scalable monitoring.

• Infrastructure Reliability: Achieve near-perfect system uptime through proactive monitoring and automated incident detection across all AWS-hosted corporate systems.

• AIOps Integration: Deploy AI-driven alert correlation to reduce noise, accelerate root cause analysis, and enable intelligent incident escalation.

• Operational Efficiency: Significantly reduce mean time to resolution by integrating comprehensive observability across infrastructure, application, and network layers.

• Scalable Monitoring: Establish a unified monitoring framework capable of scaling with global event operations and seasonal demand spikes.

The Solution

Myridius migrated the corporate platform to AWS and deployed a multi-layered observability stack combining real-time monitoring, application performance management, and AI-driven incident correlation. The team orchestrated infrastructure and application visibility, embedded AI-driven correlation and proactive thresholds into operations, and reimagined incident management as a proactive, intelligent discipline. The progression moved from deploying foundational AWS and application monitoring, to embedding AIOps-driven correlation, to reimagining operations through a proactive, automated escalation framework.

• Orchestrated the foundation: Deployed Amazon CloudWatch for infrastructure monitoring, log collection, and threshold-based alarms across critical resources including CPU, memory, disk utilization, and IOPS, and implemented DataDog for real-time application performance monitoring and centralized log analysis.

• Embedded intelligence into operations: Integrated BigPanda for AI-powered alert correlation, automatically consolidating alerts from multiple monitoring sources to reduce noise and surface actionable incidents for faster resolution.

• Reimagined the operating model: Established predefined thresholds for every monitored resource with automated escalation workflows through PagerDuty, ensuring breaches are identified and addressed before they impact business operations.

Governance and Trust

Because this engagement protected mission-critical event and ticketing operations, reliability and operational control were central. Predefined thresholds for every monitored resource, combined with automated escalation through PagerDuty, ensured that breaches were caught and routed for action before they affected fans.

AI-driven alert correlation through BigPanda reduced noise so that operations teams could focus on genuine incidents, while DataDog and CloudWatch provided transparent, real-time visibility into latency, error rates, and resource utilization. Improved monitoring also supported change tracking and compliance, and the framework was designed to scale with seasonal surges so that control held even at peak demand.

Results

The deployment transformed a strained, reactive infrastructure operation into a proactive, AI-driven, and highly available observability model. The team resolved incidents faster while maintaining near-perfect uptime through seasonal peaks.

The result:

• 99.95 percent system availability through proactive monitoring, automated alerting, and rapid incident response across all corporate systems.

• A 50 percent reduction in mean time to resolution, leveraging DataDog's deep diagnostics and BigPanda's intelligent alert correlation for faster root cause identification.

• Real-time observability into latency, error rates, and resource utilization, with reduced alert fatigue and monitoring that scales dynamically with seasonal event surges.

Technology Stack

Cloud Platform

AWS (CloudWatch, EC2, S3)
Hosts corporate infrastructure and foundational monitoring

Application Monitoring

DataDog (APM, infrastructure monitoring, log analysis)
Provides deep real-time visibility into performance

AIOps and Incident Management

BigPanda (AI-driven alert correlation)
Consolidates alerts to surface actionable incidents

Alerting and Escalation

PagerDuty, email notifications
Route breaches for rapid, automated escalation

For a global ticketing and live entertainment leader, every minute of downtime risks the fan experience at scale. This case shows how AIOps-driven observability turns reactive firefighting into proactive, intelligent operations. This was not a monitoring add-on. It was a shift to a proactive, AI-driven observability operating model.