This is a playbook from AWS for running your IT operations with a ‘smart autopilot.’ It explains how to use AI to watch logs, metrics, and alerts so it can spot problems early, suggest fixes, and sometimes even act automatically—before users notice something is broken.
Traditional IT operations teams drown in logs, tickets, and alerts and react after outages or performance issues have already impacted customers. This guidance shows how to use AI/ML on AWS to detect anomalies, predict incidents, and automate responses, reducing downtime, noise, and manual ops effort.
Tight integration into AWS-native services and telemetry (CloudWatch, CloudTrail, X-Ray, etc.), prescriptive patterns aligned with AWS best practices, and the ability to leverage a customer’s own operational data (logs, metrics, incidents) as proprietary training and tuning fuel for AIOps models.
Hybrid
Vector Search
Medium (Integration logic)
Volume and velocity of observability data (logs/metrics/traces) driving ML cost and latency for anomaly detection and correlation; potential context-window and inference cost limits if LLMs are used for summarization and remediation suggestions.
Early Majority
This is not a standalone SaaS tool but a prescriptive blueprint for assembling AIOps capabilities from AWS-native building blocks, allowing enterprises to embed AI into their existing AWS operations stack instead of adopting a separate, monolithic AIOps platform.