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Course Outline
Advanced Architecture and Strategic Framework for AIOps
- Evaluation of AIOps platform components and infrastructure stacks
- Development of scalable AIOps data pipelines
- Strategies for service observability and telemetry management
Data Standardization and Correlation Methodologies
- Ingestion of logs, metrics, events, and trace data
- Processes for data cleaning, normalization, and context mapping
- Techniques for event correlation and noise mitigation
Anomaly Detection and Machine Learning Capabilities
- Implementation of advanced anomaly detection models using statistical and machine learning approaches
- Procedures for model training, validation, and continuous optimization
- Management of unbalanced and high-dimensional datasets
Root Cause Analysis and Predictive Analytics
- Mechanisms for machine learning-driven root cause identification
- Predictive modeling for incident forecasting
- Deployment of RCA dashboards and temporal analysis tools
Platform Tools and Laboratory Exercises
- Practical application using platforms such as Splunk ITSI, Moogsoft, Dynatrace, and IBM Watson AIOps
- Integration with ITSM systems (ServiceNow, Jira) and DevOps toolchains for government
- Development of playbooks and automated pipelines
AIOps Integration Across Cloud Environments
- Deployment of AIOps solutions within AWS, Azure, and GCP infrastructures
- Observability patterns for multi-cloud environments
- Capacity forecasting and predictive scaling strategies
Automation and Self-Healing Operational Workflows
- Design of closed-loop automation frameworks
- Configuration of runbooks, playbooks, and event triggers
- Implementation of self-healing mechanisms and resilience patterns
Practical Applications and Industry Best Practices
- Analysis of case studies across diverse sectors
- Correlation of operational metrics with business outcomes
- Strategies for continuous optimization and tuning
Summary and Future Action Items
Requirements
- Fulfillment of AIOps Foundation certification or equivalent foundational competencies
- Familiarity with data analytics, fundamental machine learning principles, and IT incident management procedures
- Professional background in IT operations, Site Reliability Engineering, or DevOps frameworks
Audience
- Senior IT operations engineers and technical architects
- Administrators responsible for AIOps platforms and implementation teams
- Site Reliability Engineers (SRE)
- DevOps platform personnel and observability specialists engaged in public sector infrastructure management for government entities
21 Hours