AIOps Fundamentals: Monitoring, Correlation, and Intelligent Alerting Training Course

Introduction to AIOps

• Defining AIOps and its strategic value.
• Comparing traditional monitoring with AIOps-driven observability.
• Examining AIOps architecture and essential components.

Operational Data Collection and Normalization

• Identifying observability data types: metrics, logs, and traces.
• Ingesting data from diverse sources such as servers, containers, and cloud environments.
• Deploying agents and exporters (e.g., Prometheus, Beats, Fluentd).

Data Correlation and Anomaly Detection

• Applying time series correlation and statistical analysis.
• Leveraging ML models for anomaly identification.
• Detecting incidents within distributed system landscapes.

Alert Management and Noise Mitigation

• Crafting intelligent alert rules and dynamic thresholds.
• Implementing suppression, deduplication, and alert grouping strategies.
• Integrating with platforms like Alertmanager, Slack, PagerDuty, or Opsgenie.

Root Cause Analysis and Visualization

• Utilizing dashboards to visualize metrics and uncover trends.
• Analyzing event timelines to support RCA.
• Tracing issues across layers using distributed tracing tools.

Automation and Remediation Strategies

• Triggering automated scripts or workflows in response to incidents.
• Integrating with ITSM systems such as ServiceNow or Jira.
• Exploring use cases including self-healing, scaling, and traffic rerouting.

Open Source and Commercial AIOps Ecosystems

• Surveying key tools: Prometheus, Grafana, ELK, Moogsoft, and Dynatrace.
• Evaluating criteria for selecting an appropriate AIOps platform.
• Conducting demonstrations and hands-on sessions with a chosen stack.

Summary and Path Forward