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

Introduction to Predictive AIOps

• Overview of predictive analytics within IT operations.
• Key data sources for prediction, including logs, metrics, and events.
• Fundamental concepts in time-series forecasting and anomaly detection.

Designing Incident Prediction Models

• Labeling historical incident data and system behaviors.
• Selecting and training models (such as LSTM, Random Forest, and 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.
• Addressing noise and missing data within operational pipelines.

Automating Root Cause Analysis (RCA)

• Applying graph-based correlation to services and infrastructure.
• Leveraging ML to infer likely root causes from event chains.
• Visualizing RCA insights using topology-aware dashboards.

Remediation and Workflow Automation

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

Scaling Intelligent AIOps Pipelines

• MLOps for observability: retraining strategies and model versioning.
• Executing real-time predictions across distributed nodes.
• Best practices for deploying AIOps in production environments.

Case Studies and Practical Applications

• Analyzing real-world incident data using predictive AIOps models.
• Deploying RCA pipelines using synthetic and production data.
• Reviewing industry use cases, including cloud outages, microservice instability, and network degradation.

Summary and Next Steps