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

Introduction to Predictive AIOps

• The role of predictive analytics in modern IT operations
• Data inputs for prediction, including logs, metrics, and events
• Core concepts in time-series forecasting and identifying anomaly patterns

Developing Incident Prediction Models

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

Data Collection and Feature Engineering

• Preparing log and metric data for model consumption
• Extracting relevant features from both structured and unstructured sources
• Addressing data noise and missing values in operational flows

Automating Root Cause Analysis (RCA)

• Establishing graph-based connections between services and infrastructure
• Leveraging ML to deduce likely root causes from event sequences
• Presenting RCA insights through topology-aware visual dashboards

Remediation and Workflow Automation

• Connecting with automation tools (e.g., Ansible, Rundeck)
• Executing rollbacks, service restarts, or traffic rerouting
• Logging and documenting automated corrective actions

Scaling Intelligent AIOps Pipelines

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

Case Studies and Real-World Applications

• Examining actual incident data with predictive AIOps techniques
• Implementing RCA pipelines using both synthetic and live data
• Analyzing industry scenarios: cloud failures, microservice instability, and network issues

Recap and Future Directions