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Course Outline
Introduction to AIOps with Open Source Tools for Government
- Overview of AIOps concepts and benefits for government operations
- Utilizing Prometheus and Grafana in the observability stack for enhanced monitoring and data visualization
- The role of machine learning (ML) in AIOps: predictive versus reactive analytics for improved decision-making
Setting Up Prometheus and Grafana for Government Use
- Installing and configuring Prometheus to collect time series data for government systems
- Creating dashboards in Grafana to display real-time metrics from government applications
- Exploring exporters, relabeling, and service discovery to enhance data collection processes for government operations
Data Preprocessing for ML in Government Applications
- Extracting and transforming Prometheus metrics for use in machine learning models within the public sector
- Preparing datasets to support anomaly detection and forecasting needs for government services
- Utilizing Grafana’s transformation features or Python pipelines to streamline data preparation processes for government
Applying Machine Learning for Anomaly Detection in Government Systems
- Implementing basic ML models for outlier detection, such as Isolation Forest and One-Class SVM, tailored for government datasets
- Training and evaluating these models on time series data from government operations
- Visualizing detected anomalies in Grafana dashboards to support real-time monitoring and response for government agencies
Forecasting Metrics with Machine Learning for Government Operations
- Building simple forecasting models, including ARIMA, Prophet, and an introduction to LSTM, to predict future trends in government systems
- Predicting system load or resource usage to optimize government IT infrastructure
- Leveraging these predictions for early alerting and scaling decisions within government operations
Integrating Machine Learning with Alerting and Automation in Government Workflows
- Defining alert rules based on ML output or predefined thresholds to enhance government IT monitoring
- Using Alertmanager for notification routing to ensure timely alerts for government staff
- Triggering scripts or automation workflows in response to detected anomalies to improve operational efficiency for government
Scaling and Operationalizing AIOps for Government Operations
- Integrating external observability tools, such as the ELK stack, Moogsoft, and Dynatrace, to enhance AIOps capabilities in government agencies
- Operationalizing ML models within observability pipelines to support continuous monitoring and improvement for government services
- Best practices for scaling AIOps initiatives to meet the unique needs of government operations
Summary and Next Steps for Government Implementation
Requirements
- A comprehensive understanding of system monitoring and observability concepts for government operations.
- Practical experience using Grafana or Prometheus in a professional setting.
- Familiarity with Python programming and foundational machine learning principles.
Audience
- Observability engineers for government agencies.
- Infrastructure and DevOps teams within the public sector.
- Monitoring platform architects and site reliability engineers (SREs) for government organizations.
14 Hours
