UST SmartOps Anomaly Detection - AIOps Monitoring Service

Static thresholds either fire too often (false positives) or miss slow degradation until it is already an outage (false negatives). UST SmartOps Anomaly Detection is a professional services engagement that builds dynamic baselines for each metric and entity - CPU, latency, queue depth, error rate, throughput - adapting to normal seasonality and workload patterns, and flagging statistically significant deviations as they emerge.

Detected anomalies are enriched with context (affected entity, deviation magnitude, historical comparison) and passed downstream to correlation and resolution, so early signals can trigger investigation or automated remediation before customers notice impact.

When you engage UST for SmartOps Anomaly Detection, you follow a structured delivery process:

Phase 1 - Discovery and Scoping (Weeks 1-2) UST engineers assess your current monitoring infrastructure, identify critical metrics and entities, and define engagement scope. Deliverable: Discovery Report with recommended baseline configuration and coverage plan.

Phase 2 - Baseline Configuration (Weeks 3-5) Dynamic baselines are configured per metric and per entity across your environments. Seasonality patterns, business cycles, and workload characteristics are mapped. Deliverable: Baseline Configuration Document with sensitivity settings per environment or service tier.

Phase 3 - Tuning and Validation (Weeks 6-8) Multivariate anomaly models are tuned against real production data. False positive rates are reduced through iterative sensitivity adjustment. Deliverable: Tuned Anomaly Models with validation report showing detection accuracy.

Phase 4 - Handoff and Enablement (Weeks 9-10) Native hand-off to AI Correlation and SmartResolution is configured. Runbook integration and team enablement sessions are delivered. Deliverable: Operational Runbooks, integration documentation, and knowledge transfer sessions.

SmartOps Anomaly Detection is built to leverage your existing AWS infrastructure:

• Amazon CloudWatch - Primary metric ingestion and monitoring source for dynamic baselining
• AWS X-Ray - Distributed tracing and latency analysis for detecting abnormal latency creep on customer-facing services
• Amazon ECS and EKS - Containerized microservice monitoring for memory leak and resource trend detection
• Amazon API Gateway - Latency and traffic pattern monitoring to identify off-pattern traffic indicating emerging incidents
• AWS Lambda - Silent job failure detection for scheduled functions that stop running without error
• AWS Step Functions - Scheduled job monitoring to spot quiet failures in orchestrated workflows
• Multi-region AWS deployments - Off-pattern traffic detection across regions for comprehensive coverage

• Per-metric, per-entity dynamic baselining
• Seasonality and business-cycle awareness
• Multivariate anomaly detection across related metrics
• Configurable sensitivity per environment or service tier
• Native hand-off to AI Correlation and SmartResolution

• Surfaces early warning signs before thresholds would fire
• Reduces false positives from rigid static rules
• Shrinks mean time to detect (MTTD) for slow-building issues
• Prioritizes attention on statistically meaningful deviations, not noise
• Improves capacity planning through trend visibility

• E-commerce platforms on ECS/EKS - Detecting a gradual memory leak or disk-fill trend across containerized microservices before customer-facing failure, with automated ticket creation in your incident management system
• Financial services APIs - Flagging abnormal latency creep on API Gateway endpoints processing thousands of transactions per minute, triggering investigation before SLA breach
• Multi-region SaaS applications - Identifying off-pattern traffic across AWS regions that may indicate an emerging incident or security concern
• Serverless and orchestrated workloads - Spotting quiet failures in Lambda functions or Step Functions workflows, such as a scheduled job that stops running silently without generating errors

• Active AWS environment with Amazon CloudWatch enabled for target workloads
• Minimum 2-4 weeks of historical metric data for effective baseline creation
• Access to relevant AWS services (X-Ray, ECS/EKS, API Gateway, Lambda, or Step Functions) depending on scope
• Designated technical point of contact for discovery and configuration phases

To begin your SmartOps Anomaly Detection engagement, request a 30-minute discovery call through AWS Marketplace Messaging. UST will scope your environment, identify high-value detection targets, and provide a tailored engagement plan.