Course Outline

Introduction to AIOps with Open Source Tools

  • Overview of AIOps concepts and benefits
  • Prometheus and Grafana in the observability stack
  • Where ML fits in AIOps: predictive vs. reactive analytics

Setting Up Prometheus and Grafana

  • Installing and configuring Prometheus for time series 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 for anomaly detection and forecasting
  • Using Grafana’s transformations 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 anomalies in Grafana dashboards

Forecasting Metrics with ML

  • Building simple forecasting models (ARIMA, Prophet, LSTM intro)
  • Predicting system load or resource usage
  • Using predictions for early alerting and scaling decisions

Integrating ML with Alerting and Automation

  • Defining alert rules based on ML output or thresholds
  • Using Alertmanager and notification routing
  • Triggering scripts or automation workflows on anomaly detection

Scaling and Operationalizing AIOps

  • Integrating external observability tools (e.g., ELK stack, Moogsoft, Dynatrace)
  • Operationalizing ML models in observability pipelines
  • Best practices for AIOps at scale

Summary and Next Steps

Requirements

  • An understanding of system monitoring and observability concepts
  • Experience using Grafana or Prometheus
  • Familiarity with Python and basic machine learning principles

Audience

  • Observability engineers
  • Infrastructure and DevOps teams
  • Monitoring platform architects and site reliability engineers (SREs)
 14 Hours

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