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

Introduction to Predictive AIOps

• An overview of predictive analytics within IT operations.
• Data sources for prediction, including logs, metrics, and events.
• Core concepts in time-series forecasting and identifying anomaly patterns.

Designing Incident Prediction Models

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

Data Collection and Feature Engineering

• Ingesting and aligning log and metric data for model input.
• Extracting features from both structured and unstructured data.
• Managing noise and missing data within operational pipelines.

Automating Root Cause Analysis (RCA)

• Graph-based correlation of services and infrastructure.
• Leveraging ML to infer probable root causes from event chains.
• Visualizing RCA using topology-aware dashboards.

Remediation and Workflow Automation

• Integrating with automation platforms (e.g., Ansible, Rundeck).
• Triggering rollbacks, restarts, or traffic redirection.
• Auditing and documenting automated interventions.

Scaling Intelligent AIOps Pipelines

• MLOps for observability, focusing on retraining and model versioning.
• Running real-time predictions across distributed nodes.
• Best practices for deploying AIOps in production environments.

Case Studies and Practical Applications

• Analysing real incident data using predictive AIOps models.
• Deploying RCA pipelines using both synthetic and production data.
• A review of industry use cases: cloud outages, microservices instability, and network degradations.

Summary and Next Steps