Course Outline

Advanced AIOps Architecture and Strategy for Government

  • Review of AIOps platform stacks and components for government
  • Designing scalable AIOps pipelines for government operations
  • Service observability and telemetry strategy for enhanced governance

Data Normalization and Correlation for Government

  • Ingesting logs, metrics, events, and traces from various sources for government systems
  • Data cleaning, normalization, and context mapping to ensure accuracy and reliability
  • Event correlation and noise reduction techniques to improve operational efficiency

Anomaly Detection and Machine Learning Extensions for Government

  • Advanced anomaly detection models (statistical and ML-driven) tailored for government use cases
  • Model training, validation, and continuous tuning to maintain high performance standards
  • Handling unbalanced and high-dimensional datasets common in government data environments

Root Cause Analysis and Predictive Analytics for Government

  • ML-based root cause workflows to enhance incident resolution for government agencies
  • Predictive modeling for incident forecasting to improve proactive management
  • Implementing RCA dashboards and timelines to support transparent and accountable operations

Tools and Platform Labs for Government

  • Hands-on labs with tools such as Splunk ITSI, Moogsoft, Dynatrace, IBM Watson AIOps tailored for government use
  • Integrations with ITSM (ServiceNow, Jira) and DevOps toolchains to streamline operations
  • Playbooks and automation pipelines to enhance operational efficiency

Cloud, Multi-Cloud, and Hybrid AIOps Integration for Government

  • AIOps in AWS, Azure, and GCP environments to support government cloud strategies
  • Multi-cloud observability patterns to ensure seamless monitoring across platforms
  • Capacity forecasting and predictive scaling to optimize resource utilization for government agencies

Automation and Self-Healing Workflows for Government

  • Designing closed-loop automation to enhance operational resilience
  • Runbooks, playbooks, and event triggers to support rapid response
  • Self-healing and resilience patterns to ensure continuous service delivery

Real-World Use Cases and Best Practices for Government

  • Case studies across industries with a focus on government applications
  • Operational metrics correlation with business outcomes to drive informed decision-making
  • Optimization and tuning strategies to maximize the effectiveness of AIOps for government operations

Summary and Next Steps for Government

Requirements

  • Completion of the AIOps Foundation course or equivalent foundational knowledge
  • Proficiency in data analytics, machine learning basics, and IT incident management processes
  • Experience in IT operations, site reliability engineering (SRE), or DevOps environments

Audience

  • Advanced IT operations engineers and architects for government
  • AIOps tool administrators and implementers
  • Site Reliability Engineers (SRE)
  • DevOps platform and observability teams
 21 Hours

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