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Course Outline
Introduction to AIOps
- Understanding AIOps and its significance.
- Contrasting traditional monitoring with AIOps-driven observability.
- Exploring AIOps architecture and essential components.
Collecting and Normalizing Operational Data
- Identifying types of observability data: metrics, logs, and traces.
- Ingesting data from diverse sources such as servers, containers, and cloud environments.
- Utilizing agents and exporters like Prometheus, Beats, and Fluentd.
Data Correlation and Anomaly Detection
- Employing time series correlation and statistical methods.
- Applying ML models for effective anomaly detection.
- Identifying incidents across distributed systems.
Alerting and Noise Reduction
- Designing intelligent alert rules and thresholds.
- Implementing suppression, deduplication, and alert grouping strategies.
- Integrating with platforms such as Alertmanager, Slack, PagerDuty, or Opsgenie.
Root Cause Analysis and Visualization
- Utilizing dashboards to visualize metrics and identify trends.
- Examining events and timelines to facilitate RCA (Root Cause Analysis).
- Tracing issues across layers using distributed tracing tools.
Automation and Remediation
- Triggering automated scripts or workflows triggered by incidents.
- Integrating with ITSM systems like ServiceNow and Jira.
- Reviewing use cases such as self-healing, scaling, and traffic rerouting.
Open Source and Commercial AIOps Platforms
- Overview of tools including Prometheus, Grafana, ELK, Moogsoft, and Dynatrace.
- Establishing evaluation criteria for selecting an appropriate AIOps platform.
- Participating in 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.
- Prior experience with monitoring tools or dashboards.
- Familiarity with basic log and metric formats.
Audience
- Operations teams managing infrastructure and applications.
- Site Reliability Engineers (SREs).
- Teams focused on IT monitoring and observability.
14 Hours
