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

Introduction to Predictive AIOps

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

Developing Incident Prediction Models

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

Data Ingestion and Feature Engineering

• Collecting and aligning log and metric data for model consumption
• Extracting features from both structured and unstructured datasets
• Managing noise and missing values within operational data pipelines

Automating Root Cause Analysis (RCA)

• Utilizing graph-based methods to correlate services and infrastructure
• Applying ML to deduce probable root causes from event sequences
• Visualizing RCA insights through topology-aware dashboards

Remediation and Workflow Automation

• Integrating with automation tools such as Ansible and Rundeck
• Automating responses like rollbacks, restarts, or traffic rerouting
• Maintaining audit trails and documentation for automated actions

Scaling Intelligent AIOps Pipelines

• Implementing MLOps for observability, including retraining and model versioning
• Executing real-time predictions across distributed nodes
• Best practices for deploying AIOps in live production settings

Case Studies and Real-World Applications

• Applying predictive AIOps models to analyze real-world incident data
• Deploying RCA pipelines using both synthetic and production datasets
• Examining industry use cases: cloud outages, microservice instability, and network performance issues

Wrap-Up and Future Directions