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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 place of ML in AIOps: distinguishing between predictive and reactive analytics.
Setting Up Prometheus and Grafana
- Installing and configuring Prometheus for time series data collection.
- Building dashboards in Grafana utilizing real-time metrics.
- Exploring exporters, relabeling, and service discovery mechanisms.
Data Preprocessing for ML
- Extracting and transforming Prometheus metrics.
- Preparing datasets suitable for anomaly detection and forecasting.
- Utilizing Grafana’s transformation features or Python pipelines.
Applying Machine Learning for Anomaly Detection
- Basic ML models for outlier detection (e.g., Isolation Forest, One-Class SVM).
- Training and evaluating models on time series data.
- Visualizing detected anomalies within Grafana dashboards.
Forecasting Metrics with ML
- Building simple forecasting models (ARIMA, Prophet, LSTM introduction).
- Predicting system load or resource usage patterns.
- Leveraging predictions for proactive alerting and scaling decisions.
Integrating ML with Alerting and Automation
- Defining alert rules based on ML outputs or static thresholds.
- Utilizing 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 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 programming and fundamental machine learning principles.
Target Audience
- Observability engineers.
- Infrastructure and DevOps teams.
- Monitoring platform architects and Site Reliability Engineers (SREs).
14 Hours
