Course Objective & Scope
In cloud and microservice environments, alert storms from thousands of metrics and log streams make it hard for operations teams to spot the real failure. Aligned with the AIOps Foundation syllabus, this programme explains how machine learning and generative AI transform IT operations.
Preparing observability data, time-series anomaly detection, topology- and time-based event correlation, automated root-cause analysis (RCA), LLM/agent-assisted incident summarisation and runbook automation are explored through practical cases. Participants build an AIOps use-case scorecard and adoption roadmap for their monitoring stack.
Who Should Attend?
- NOC, SOC, SRE, DevOps and IT operations (ITOps) engineers
- IT managers responsible for ITSM, incident management and observability platforms
- Technology professionals preparing for the AIOps Foundation exam
- Prerequisite: basic familiarity with IT operations and monitoring concepts
Key Takeaways
- Explain the ingestion, detection, inference and automation layers of an AIOps architecture
- Replace brittle static thresholds with seasonality-aware dynamic anomaly detection
- Cut alert fatigue through event deduplication, suppression and correlation
- Accelerate root-cause analysis (MTTR) using log clustering, topology maps and LLM assistants
- Plan AIOps tool selection, data prerequisites and human-in-the-loop remediation workflows
Curriculum
AIOps Concepts, Architecture and Business Value
- Moving from traditional ITOps to AIOps, the Gartner framework and links to DevOps/SRE/ITSM
- MTTD, MTTR, alert noise reduction and capacity forecasting metrics
Telemetry Data and Anomaly Detection
- Unifying metrics, logs, traces, events and service topology data
- Time-series models, seasonality and log parsing/clustering
Event Correlation and Root-Cause Analysis (RCA)
- Deduplication, time-window and topology-aware event correlation
- Causal analysis and correlating alerts with recent deployments or config changes
GenAI/Agentic AIOps and Automated Remediation
- LLM-assisted incident summaries, postmortem drafts and natural-language telemetry queries
- Runbook automation, self-healing and human-in-the-loop guardrails
Enterprise AIOps Adoption and Exam Review
- Evaluating the platform landscape (Dynatrace, Datadog, Splunk, Elastic, BigPanda)
- AIOps Foundation concept review and sample practice questions
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