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

Introduction to Predictive AIOps

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

Crafting Incident Prediction Models

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

Data Acquisition and Feature Engineering

• Ingesting and synchronizing log and metric data for model consumption
• Extracting features from both structured and unstructured data
• Managing noise and missing values in operational pipelines

Streamlining Root Cause Analysis (RCA)

• Graph-based correlation of services and infrastructure components
• Leveraging ML to deduce likely root causes from event sequences
• Presenting RCA findings via topology-aware dashboards

Remediation and Process Automation

• Connecting with automation platforms (e.g., Ansible, Rundeck)
• Initiating rollbacks, restarts, or traffic shifting
• Auditing and recording automated actions

Scaling Intelligent AIOps Pipelines

• MLOps for observability: model retraining and version control
• Executing real-time predictions across distributed nodes
• Best practices for implementing AIOps in live production environments

Case Studies and Real-World Applications

• Examining actual incident data with predictive AIOps models
• Deploying RCA pipelines using both synthetic and live production data
• Reviewing industry scenarios: cloud outages, microservice instability, network performance drops

Conclusion and Future Steps