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

Introduction to Predictive AIOps

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

Designing Incident Prediction Models

• Categorizing historical 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 synchronizing log and metric data for model input
• Extracting features from both structured and unstructured data
• Managing noise and missing data in operational workflows

Automating Root Cause Analysis (RCA)

• Graph-based analysis of service and infrastructure correlations
• Leveraging ML to deduce likely root causes from event sequences
• Presenting RCA insights via topology-aware dashboards

Remediation and Workflow Automation

• Integration with automation tools (e.g., Ansible, Rundeck)
• Initiating rollbacks, restarts, or traffic shifts
• Auditing and recording automated actions

Scaling Intelligent AIOps Pipelines

• MLOps for observability: retraining and version control for models
• Executing real-time predictions across distributed systems
• Best practices for AIOps deployment in live environments

Case Studies and Practical Applications

• Examining actual incident data using predictive AIOps models
• Rolling out RCA pipelines using synthetic and live data
• Reviewing industry examples: cloud failures, microservice instability, network issues

Wrap-up and Future Steps