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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

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