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Course Outline
Advanced AIOps Architecture and Strategy
- Review of AIOps platform stacks and components
- Designing scalable AIOps pipelines
- Service observability and telemetry strategy
Data Normalization and Correlation
- Ingesting logs, metrics, events, and traces
- Data cleaning, normalization, and context mapping
- Event correlation and noise reduction techniques
Anomaly Detection and Machine Learning Extensions
- Advanced anomaly detection models (statistical and ML-driven)
- Model training, validation, and continuous tuning
- Handling unbalanced and high-dimensional datasets
Root Cause Analysis and Predictive Analytics
- ML-based root cause workflows
- Predictive modeling for incident forecasting
- Implementing RCA dashboards and timelines
Tools and Platform Labs
- Hands-on labs with tools such as Splunk ITSI, Moogsoft, Dynatrace, IBM Watson AIOps
- Integrations with ITSM (ServiceNow, Jira) and DevOps toolchains
- Playbooks and automation pipelines
Cloud, Multi-Cloud, and Hybrid AIOps Integration
- AIOps in AWS, Azure, and GCP environments
- Multi-cloud observability patterns
- Capacity forecasting and predictive scaling
Automation and Self-Healing Workflows
- Designing closed-loop automation
- Runbooks, playbooks, and event triggers
- Self-healing and resilience patterns
Real-World Use Cases and Best Practices
- Case studies across industries
- Operational metrics correlation with business outcomes
- Optimization and tuning strategies
Summary and Next Steps
Requirements
- Completion of AIOps Foundation or equivalent foundational knowledge
- Comfort with data analytics, ML basics, and IT incident processes
- Experience in IT operations, SRE, or DevOps environments
Audience
- Advanced IT operations engineers and architects
- AIOps tool administrators and implementers
- Site Reliability Engineers (SRE)
- DevOps platform and observability teams
21 Hours