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Course Outline
Introduction to Predictive AIOps for Government
- Overview of predictive analytics in IT operations for government agencies
- Data sources for prediction, including logs, metrics, and events
- Key concepts in time-series forecasting and anomaly detection patterns
Designing Incident Prediction Models for Government
- Labeling historical incidents and system behavior for accurate model training
- Selecting and training models, such as LSTM, Random Forest, and AutoML, tailored for government use cases
- Evaluating model performance and managing false positives to ensure reliable operations
Data Collection and Feature Engineering for Government IT Operations
- Ingesting and aligning log and metric data for effective model input in government systems
- Extracting features from both structured and unstructured data to enhance predictive accuracy
- Managing noise and missing data in operational pipelines to maintain data integrity
Automating Root Cause Analysis (RCA) for Government IT Systems
- Utilizing graph-based correlation of services and infrastructure to identify root causes
- Leveraging machine learning to infer probable root causes from event chains in government environments
- Visualizing RCA with topology-aware dashboards for enhanced situational awareness
Remediation and Workflow Automation for Government IT Operations
- Integrating with automation platforms such as Ansible and Rundeck to streamline remediation efforts
- Triggering automated actions like rollbacks, restarts, or traffic redirection to quickly resolve issues
- Auditing and documenting automated interventions to ensure transparency and accountability
Scaling Intelligent AIOps Pipelines for Government IT Environments
- Implementing MLOps practices for observability, including retraining and model versioning
- Running real-time predictions across distributed nodes to enhance operational efficiency
- Best practices for deploying AIOps in production environments within government agencies
Case Studies and Practical Applications of Predictive AIOps for Government
- Analyzing real incident data using predictive AIOps models to improve service reliability
- Deploying RCA pipelines with both synthetic and production data to enhance root cause identification
- Review of industry use cases relevant to government, including cloud outages, microservices instability, and network degradations
Summary and Next Steps for Government AIOps Implementation
Requirements
- Experience with monitoring systems such as Prometheus or ELK for government operations
- Working knowledge of Python and foundational machine learning techniques
- Familiarity with incident management workflows in a public sector environment
Audience
- Senior Site Reliability Engineers (SREs) for government agencies
- IT Automation Architects in the public sector
- DevOps and Observability Platform Leads within government organizations
14 Hours
