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

Introduction to Predictive AIOps

• Overview of predictive analytics in IT operations.
• Data sources used for predictions (logs, metrics, events).
• Core concepts in time-series forecasting and anomaly detection.

Designing Incident Prediction Models

• Labeling historical incidents and system behaviors.
• 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.
• Utilizing ML to infer likely root causes from event chains.
• Visualizing RCA outcomes 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: model retraining and versioning.
• Executing predictions in real-time across distributed nodes.
• Best practices for deploying AIOps in production.

Case Studies and Practical Applications

• Analyzing real incident data using predictive AIOps models.
• Deploying RCA pipelines with synthetic and production data.
• Review of industry use cases: cloud outages, microservices instability, network degradations.

Summary and Next Steps