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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 (logs, metrics, events) within government systems
- Key concepts in time-series forecasting and anomaly patterns for government use cases
Designing Incident Prediction Models for Government
- Labeling historical incidents and system behavior to enhance predictive models for government operations
- Choosing and training models (e.g., LSTM, Random Forest, AutoML) tailored for government needs
- Evaluating model performance and managing false positives in a government context
Data Collection and Feature Engineering for Government
- Ingesting and aligning log and metric data for model input to support government operations
- Extracting features from structured and unstructured data in government systems
- Handling noise and missing data in operational pipelines within government environments
Automating Root Cause Analysis (RCA) for Government
- Graph-based correlation of services and infrastructure to improve RCA in government IT
- Using machine learning to infer probable root causes from event chains within government systems
- Visualizing RCA with topology-aware dashboards for enhanced transparency in government operations
Remediation and Workflow Automation for Government
- Integrating with automation platforms (e.g., Ansible, Rundeck) to support government IT workflows
- Triggering rollbacks, restarts, or traffic redirection in response to incidents within government systems
- Auditing and documenting automated interventions for accountability and compliance in government operations
Scaling Intelligent AIOps Pipelines for Government
- MLOps for observability: retraining and model versioning to maintain accuracy in government IT
- Running predictions in real-time across distributed nodes to support efficient government operations
- Best practices for deploying AIOps in production environments within the public sector
Case Studies and Practical Applications for Government
- Analyzing real incident data using predictive AIOps models to enhance government IT operations
- Deploying RCA pipelines with synthetic and production data to improve government service reliability
- Review of industry use cases: cloud outages, microservices instability, network degradations in a government context
Summary and Next Steps for Government
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 for government organizations
- DevOps and observability platform leads for government initiatives
14 Hours
