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

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