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Course Outline
Introduction to AIOps
- Defining AIOps and its significance.
- Contrasting traditional monitoring with AIOps-driven observability.
- Exploring AIOps architecture and key components.
Collecting and Normalizing Operational Data
- Types of observability data: metrics, logs, and traces.
- Ingesting data from diverse sources, including servers, containers, and cloud environments.
- Utilizing agents and exporters such as Prometheus, Beats, and Fluentd.
Data Correlation and Anomaly Detection
- Employing time series correlation and statistical methods.
- Applying ML models for anomaly detection.
- Detecting incidents across distributed systems.
Alerting and Noise Reduction
- Designing intelligent alert rules and thresholds.
- Implementing suppression, deduplication, and alert grouping.
- Integrating with platforms like Alertmanager, Slack, PagerDuty, or Opsgenie.
Root Cause Analysis and Visualization
- Using dashboards to visualize metrics and identify trends.
- Exploring events and timelines for Root Cause Analysis (RCA).
- Tracing issues across layers using distributed tracing tools.
Automation and Remediation
- Triggering automated scripts or workflows in response to incidents.
- Integrating with ITSM systems such as ServiceNow and Jira.
- Examining use cases: 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 AIOps platform.
- Demo and hands-on session with a selected stack.
Summary and Next Steps
Requirements
- A solid understanding of IT operations and system monitoring concepts.
- Prior experience with monitoring tools or dashboards.
- Familiarity with fundamental log and metric formats.
Audience
- Operations teams responsible for infrastructure and applications.
- Site Reliability Engineers (SREs).
- IT monitoring and observability teams.
14 Hours
