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Course Outline

Introduction to AIOps

  • Defining AIOps and its significance.
  • Comparing traditional monitoring with AIOps-driven observability.
  • Exploring AIOps architecture and key components.

Collecting and Normalizing Operational Data

  • Understanding types of observability data: metrics, logs, and traces.
  • Ingesting data from diverse sources (servers, containers, cloud).
  • Utilizing agents and exporters (Prometheus, Beats, Fluentd).

Data Correlation and Anomaly Detection

  • Applying time series correlation and statistical methods.
  • Employing ML models for anomaly detection.
  • Identifying incidents across distributed systems.

Alerting and Noise Reduction

  • Designing intelligent alert rules and thresholds.
  • Implementing suppression, deduplication, and alert grouping.
  • Integrating with Alertmanager, Slack, PagerDuty, or Opsgenie.

Root Cause Analysis and Visualization

  • Using dashboards to visualize metrics and detect trends.
  • Exploring events and timelines for RCA.
  • Tracing issues across layers using distributed tracing tools.

Automation and Remediation

  • Triggering automated scripts or workflows from incidents.
  • Integrating with ITSM systems (ServiceNow, Jira).
  • Exploring use cases: self-healing, scaling, traffic rerouting.

Open Source and Commercial AIOps Platforms

  • Overview of tools: Prometheus, Grafana, ELK, Moogsoft, Dynatrace.
  • Understanding evaluation criteria for selecting an AIOps platform.
  • Conducting a demo and hands-on session with a selected stack.

Summary and Next Steps

Requirements

  • A foundational understanding of IT operations and system monitoring concepts.
  • Practical experience with monitoring tools or dashboards.
  • Familiarity with basic log and metric formats.

Audience

  • Operations teams responsible for infrastructure and applications.
  • Site Reliability Engineers (SREs).
  • IT monitoring and observability teams.
 14 Hours

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