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

Introduction to Predictive AIOps

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

Building Incident Prediction Models

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

Data Collection and Feature Engineering

• Processing and aligning log and metric data for model input
• Extracting features from both 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

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

Scaling Intelligent AIOps Pipelines

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

Case Studies and Practical Applications

• Examining real incident data with predictive AIOps models
• Deploying RCA pipelines using synthetic and production data
• Reviewing industry examples: cloud outages, microservice instability, network degradation

Summary and Next Steps