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

Introduction to Predictive AIOps

• An overview of predictive analytics within IT operations
• Data sources for prediction (logs, metrics, events)
• Core concepts in time-series forecasting and anomaly patterns

Creating Incident Prediction Models

• Labelling historical incidents and system behaviour
• Selecting and training models (e.g., LSTM, Random Forest, AutoML)
• Assessing model performance and managing false positives

Data Collection and Feature Engineering

• Ingesting and aligning log and metric data for model input
• Extracting features from both structured and unstructured data
• Addressing noise and missing data in operational pipelines

Automating Root Cause Analysis (RCA)

• Graph-based correlation of services and infrastructure
• Utilising ML to infer probable root causes from event chains
• Visualising RCA through topology-aware dashboards

Remediation and Workflow Automation

• Integration with automation platforms (e.g., Ansible, Rundeck)
• Triggering rollbacks, restarts, or traffic redirection
• Auditing and documenting automated interventions

Scaling Intelligent AIOps Pipelines

• MLOps for observability: retraining and model versioning
• Running predictions in real-time across distributed nodes
• Best practices for deploying AIOps in production environments

Case Studies and Practical Applications

• Analysing real incident data using predictive AIOps models
• Deploying RCA pipelines with synthetic and production data
• Review of industry use cases: cloud outages, microservices instability, network degradations

Summary and Next Steps