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Course Outline
Introduction to AIOps with Open Source Tools
- Foundational concepts and operational advantages of AIOps
- The role of Prometheus and Grafana within the observability architecture
- Integration of machine learning for predictive versus reactive analytical approaches
Configuration of Prometheus and Grafana
- Deployment and configuration of Prometheus for time-series data collection
- Development of real-time metric dashboards in Grafana
- Utilization of exporters, label relabeling, and service discovery mechanisms
Data Preprocessing for Machine Learning
- Extraction and transformation of Prometheus metric data
- Dataset preparation for anomaly detection and predictive forecasting
- Implementation of data pipelines using Grafana transformations or Python scripts
Implementation of Machine Learning for Anomaly Detection
- Deployment of statistical models for outlier identification (e.g., Isolation Forest, One-Class SVM)
- Training and validation of models using time-series datasets
- Visualization of detected anomalies within Grafana interfaces
Forecasting System Metrics via Machine Learning
- Construction of forecasting models (e.g., ARIMA, Prophet, LSTM)
- Prediction of resource consumption and system workload trends
- Application of forecasts to inform proactive alerting and scaling protocols
Integration of Machine Learning with Alerting and Automation
- Establishment of alert criteria based on machine learning outputs or static thresholds
- Configuration of Alertmanager for notification routing and management
- Execution of automated workflows or scripts in response to detected anomalies
Scaling and Operationalizing AIOps Frameworks
- Interfacing with external observability platforms (e.g., ELK stack, Moogsoft, Dynatrace)
- Embedding machine learning models within continuous observability pipelines for government use cases
- Adherence to best practices for enterprise-scale AIOps implementation
Summary and Strategic Next Steps
Requirements
- Comprehensive knowledge of system monitoring and observability frameworks
- Hands-on experience with Grafana or Prometheus environments
- Proficiency in Python and foundational machine learning methodologies
Audience
- Observability specialists
- Infrastructure and DevOps personnel
- Monitoring platform architects and site reliability engineers (SREs) engaged in federal operations for government agencies
14 Hours
