AIOps Training — Intelligent IT Operations, Event Correlation & Anomaly Detection

Who Should Attend

This program is for SREs, IT operations engineers, and NOC managers drowning in alert volume. If your team receives 1,000+ alerts daily, 80% are duplicates or false positives, and root cause analysis takes hours of manual correlation — AIOps applies machine learning to operations data so your team investigates incidents, not alerts.

Learning Outcomes

• Implement event correlation — grouping related alerts, suppressing duplicates, identifying parent-child relationships
• Deploy ML-based anomaly detection on metrics, logs, and traces with automatic baseline learning
• Configure predictive alerting that warns before disk fills, memory leaks, or performance degrades
• Build automated incident response — diagnostics, runbook execution, and resolution for known failure patterns
• Reduce alert noise by 80%+ through intelligent correlation and deduplication

Course Modules

1. AIOps Fundamentals — What AIOps is (and isn’t). AIOps vs. traditional monitoring. AIOps maturity model.
2. Observability Data Foundation — Metrics, logs, traces as ML input. Data quality for AIOps. Normalization.
3. Event Correlation — Rule-based correlation. ML-based correlation. Topological correlation. Time-based clustering.
4. Anomaly Detection — Statistical methods. ML models for anomaly detection. Baseline learning. Seasonal patterns.
5. Predictive Alerting — Trend analysis. Forecasting. Predictive thresholds. Alert before the incident.
6. AIOps Platforms — Splunk ITSI, ServiceNow ITOM, Dynatrace, Datadog, BigPanda, Moogsoft. Selection criteria.
7. Automated Incident Response — AIOps + runbook automation. Automated diagnostics. Human-in-the-loop for novel incidents.
8. AIOps Implementation — Deployment patterns. Integration with monitoring stack. Tuning correlation rules. Reducing false positives.
9. Measuring AIOps Success — Alert reduction metrics. MTTD/MTTR improvement. Operator time saved. Incident prevention rate.
10. Capstone: AIOps Deployment — Deploy event correlation, anomaly detection, and automated response for a simulated microservices environment.

Hands-on Labs (16 total)

Labs include: “Configure event correlation rules that group 50 related alerts into 2 actionable incidents,” “Train an anomaly detection model on 4 weeks of production metrics and detect injected anomalies,” “Build an automated response playbook that diagnoses a ‘high CPU’ alert and identifies the responsible service.”

Frequently Asked Questions

Does AIOps require a data science team? No. Modern AIOps platforms provide pre-built ML models for event correlation and anomaly detection. The course teaches you to configure, tune, and interpret these — not to build models from scratch. Python familiarity helps but is not required.

Will AIOps replace our existing monitoring tools? No. AIOps sits on top of your monitoring stack — it ingests alerts from Prometheus, Datadog, Splunk, Nagios, etc. and correlates them. Your monitoring tools remain the data sources; AIOps makes them manageable at scale.