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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 within the observability stack.
- The position of ML 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 ML
- Extracting and transforming Prometheus metrics.
- Preparing datasets suitable for anomaly detection and forecasting.
- Utilizing Grafana’s transformation capabilities or Python pipelines.
Applying Machine Learning for Anomaly Detection
- Employing basic ML models for outlier detection (e.g., Isolation Forest, One-Class SVM).
- Training and evaluating models on time series data.
- Visualizing anomalies within Grafana dashboards.
Forecasting Metrics with ML
- Constructing simple forecasting models (introduction to ARIMA, Prophet, LSTM).
- Predicting system load or resource usage.
- Leveraging predictions for early alerting and scaling decisions.
Integrating ML with Alerting and Automation
- Defining alert rules based on ML outputs or predefined thresholds.
- Managing Alertmanager and configuring 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 ML 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 fundamental machine learning principles.
Audience
- Observability engineers.
- Infrastructure and DevOps teams.
- Monitoring platform architects and Site Reliability Engineers (SREs).
14 Hours
