AIOps in Action: Incident Prediction and Root Cause Automation Training Course

Introduction to Predictive AIOps

• The role of predictive analytics in IT operations
• Data inputs for forecasting (logs, metrics, events)
• Core concepts in time-series prediction and anomaly detection

Crafting Incident Prediction Models

• Labeling past incidents and system behaviors
• Selecting and training algorithms (e.g., LSTM, Random Forest, AutoML)
• Assessing model accuracy and managing false positives

Data Aggregation and Feature Construction

• Aligning log and metric data for model consumption
• Extracting features from both structured and unstructured data
• Addressing noise and data gaps in operational streams

Streamlining Root Cause Analysis (RCA)

• Correlating services and infrastructure via graph-based methods
• Leveraging ML to deduce likely root causes from event sequences
• Displaying RCA insights through topology-aware dashboards

Remediation and Process Automation

• Integrating with automation tools (e.g., Ansible, Rundeck)
• Initiating rollbacks, restarts, or traffic rerouting
• Tracking and recording automated interventions

Scaling Intelligent AIOps Pipelines

• Applying MLOps to observability: model retraining and version control
• Executing real-time predictions across distributed nodes
• Best practices for deploying AIOps in production landscapes

Case Studies and Real-World Applications

• Applying predictive AIOps models to actual incident data
• Implementing RCA pipelines using synthetic and live data
• Examining industry scenarios: cloud failures, microservice instability, network performance drops

Recap and Future Steps