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Course Outline
Introduction to AIOps with Open Source Tools
- Overview of AIOps Concepts and Benefits for Government
- Prometheus and Grafana in the Observability Stack for Government
- Where Machine Learning Fits in AIOps: Predictive vs. Reactive Analytics for Government
Setting Up Prometheus and Grafana for Government
- Installing and Configuring Prometheus for Time Series Collection in Government Environments
- Creating Dashboards in Grafana Using Real-Time Metrics for Government Operations
- Exploring Exporters, Relabeling, and Service Discovery for Enhanced Observability in Government Systems
Data Preprocessing for Machine Learning in Government
- Extracting and Transforming Prometheus Metrics for Government Use Cases
- Preparing Datasets for Anomaly Detection and Forecasting in Government Systems
- Utilizing Grafana’s Transformations or Python Pipelines for Data Preparation in Government
Applying Machine Learning for Anomaly Detection in Government
- Basic Machine Learning Models for Outlier Detection (e.g., Isolation Forest, One-Class SVM) in Government Contexts
- Training and Evaluating Models on Time Series Data for Government Applications
- Visualizing Anomalies in Grafana Dashboards for Enhanced Government Monitoring
Forecasting Metrics with Machine Learning for Government
- Building Simple Forecasting Models (ARIMA, Prophet, LSTM Introduction) for Government Systems
- Predicting System Load or Resource Usage in Government Environments
- Using Predictions for Early Alerting and Scaling Decisions in Government Operations
Integrating Machine Learning with Alerting and Automation for Government
- Defining Alert Rules Based on Machine Learning Output or Thresholds in Government Systems
- Using Alertmanager and Notification Routing for Effective Communication in Government
- Triggering Scripts or Automation Workflows on Anomaly Detection for Efficient Government Operations
Scaling and Operationalizing AIOps in Government
- Integrating External Observability Tools (e.g., ELK Stack, Moogsoft, Dynatrace) for Comprehensive Monitoring in Government
- Operationalizing Machine Learning Models in Observability Pipelines for Government Efficiency
- Best Practices for AIOps at Scale in Government Operations
Summary and Next Steps for Government
Requirements
- An understanding of system monitoring and observability concepts for government.
- Experience using Grafana or Prometheus in a professional setting.
- Familiarity with Python and foundational machine learning principles.
Audience
- Observability engineers within federal agencies.
- Infrastructure and DevOps teams for government entities.
- Monitoring platform architects and site reliability engineers (SREs) in public sector organizations.
14 Hours
