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Course Outline
Introduction to AIOps with Open Source Tools
- Overview of AIOps concepts and associated benefits.
- The role of Prometheus and Grafana within the observability stack.
- The integration of ML into AIOps: comparing predictive versus reactive analytics.
Setting Up Prometheus and Grafana
- Installing and configuring Prometheus for time series data collection.
- Creating dashboards in Grafana utilizing real-time metrics.
- Exploring exporters, relabeling techniques, and service discovery mechanisms.
Data Preprocessing for ML
- Extracting and transforming Prometheus metrics.
- Preparing datasets specifically for anomaly detection and forecasting tasks.
- Utilizing Grafana’s transformations or Python-based pipelines.
Applying Machine Learning for Anomaly Detection
- Employing basic ML models for outlier detection (such as Isolation Forest and One-Class SVM).
- Training and evaluating models on time series data.
- Visualizing detected anomalies within Grafana dashboards.
Forecasting Metrics with ML
- Developing simple forecasting models (introducing ARIMA, Prophet, and LSTM).
- Predicting system load or resource usage patterns.
- Leveraging predictions to inform early alerting and scaling decisions.
Integrating ML with Alerting and Automation
- Defining alert rules based on ML outputs or established thresholds.
- Managing Alertmanager and configuring notification routing.
- Triggering scripts or automation workflows in response to detected anomalies.
Scaling and Operationalizing AIOps
- Integrating external observability tools (e.g., ELK stack, Moogsoft, Dynatrace).
- Operationalizing ML models within observability pipelines.
- Adhering to best practices for implementing AIOps at scale.
Summary and Next Steps
Requirements
- A solid understanding of system monitoring and observability concepts.
- Practical experience with Grafana or Prometheus.
- Familiarity with Python programming and fundamental machine learning principles.
Audience
- Observability engineers.
- Infrastructure and DevOps teams.
- Monitoring platform architects and Site Reliability Engineers (SREs).
14 Hours
