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

Introduction to Predictive AIOps

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

Developing Incident Prediction Models

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

Data Collection and Feature Engineering

• Ingesting and aligning log and metric data for model inputs
• Extracting features from structured and unstructured data
• Managing noise and missing data in operational pipelines

Automating Root Cause Analysis (RCA)

• Graph-based correlation of services and infrastructure
• Leveraging ML to deduce likely root causes from event chains
• Visualizing RCA through topology-aware dashboards

Remediation and Workflow Automation

• Integrating 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
• Executing real-time predictions across distributed nodes
• Best practices for deploying AIOps in production settings

Case Studies and Practical Applications

• Analyzing 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