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Course Outline
Introduction to AIOps with Open Source Tools
- Overview of AIOps concepts and benefits
- The role of Prometheus and Grafana in the observability stack
- The place of machine learning in AIOps: predictive versus reactive analytics
Setting Up Prometheus and Grafana
- Installing and configuring Prometheus for time series data collection
- Creating dashboards in Grafana using real-time metrics
- Exploring exporters, relabeling, and service discovery
Data Preprocessing for Machine Learning
- Extracting and transforming Prometheus metrics
- Preparing datasets for anomaly detection and forecasting
- Utilising Grafana’s transformations or Python pipelines
Applying Machine Learning for Anomaly Detection
- Fundamental machine learning models for outlier detection (e.g., Isolation Forest, One-Class SVM)
- Training and evaluating models on time series data
- Visualising anomalies within Grafana dashboards
Forecasting Metrics with Machine Learning
- Building simple forecasting models (ARIMA, Prophet, LSTM introduction)
- Predicting system load or resource usage
- Leveraging predictions for early alerting and scaling decisions
Integrating Machine Learning with Alerting and Automation
- Defining alert rules based on machine learning output or predefined thresholds
- Using Alertmanager and notification routing
- Triggering scripts or automation workflows upon anomaly detection
Scaling and Operationalizing AIOps
- Integrating external observability tools (e.g., ELK stack, Moogsoft, Dynatrace)
- Operationalizing machine learning models within observability pipelines
- Best practices for implementing AIOps at scale
Summary and Next Steps
Requirements
- A solid understanding of system monitoring and observability concepts
- Practical experience using Grafana or Prometheus
- Familiarity with Python and foundational machine learning principles
Audience
- Observability engineers
- Infrastructure and DevOps teams
- Monitoring platform architects and site reliability engineers (SREs)
14 Hours
