Implementing AIOps with Prometheus, Grafana, and ML Training Course

Introduction to AIOps Using Open-Source Solutions <\/p>

• Understanding AIOps concepts and their advantages <\/li>
• The role of Prometheus and Grafana in the observability stack <\/li>
• The place of ML in AIOps: predictive versus reactive analytics <\/li> <\/ul>

  Configuring Prometheus and Grafana <\/p>

  • Setting up and configuring Prometheus for time series data collection <\/li>
  • Designing Grafana dashboards with real-time metrics <\/li>
  • Investigating exporters, relabeling, and service discovery mechanisms <\/li> <\/ul>

    Data Preparation for Machine Learning <\/p>

    • Extracting and processing Prometheus metrics <\/li>
    • Structuring datasets for anomaly detection and forecasting tasks <\/li>
    • Leveraging Grafana’s transformation features or Python pipelines <\/li> <\/ul>

      Utilizing Machine Learning for Anomaly Detection <\/p>

      • Applying fundamental ML models for outlier identification (e.g., Isolation Forest, One-Class SVM) <\/li>
      • Training and assessing models against time series data <\/li>
      • Displaying detected anomalies within Grafana dashboards <\/li> <\/ul>

        Metric Forecasting via ML <\/p>

        • Developing basic forecasting models (ARIMA, Prophet, introductory LSTM) <\/li>
        • Anticipating system load or resource consumption <\/li>
        • Utilizing predictions for proactive alerting and scaling actions <\/li> <\/ul>

          Merging ML with Alerting and Automation <\/p>

          • Creating alert rules based on ML outputs or defined thresholds <\/li>
          • Implementing Alertmanager and notification routing strategies <\/li>
          • Initiating scripts or automation workflows upon anomaly detection <\/li> <\/ul>

            Scaling and Implementing AIOps Operations <\/p>

            • Connecting external observability tools (e.g., ELK stack, Moogsoft, Dynatrace) <\/li>
            • Integrating ML models into observability workflows <\/li>
            • Best practices for large-scale AIOps deployment <\/li> <\/ul>

              Recap and Future Directions <\/p>