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

Predictive AIOps Fundamentals

• Overview of analytics for prediction within IT operations
• Input data types for forecasting (logs, metrics, events)
• Essential concepts in time-series prediction and anomaly detection

Creating Incident Prediction Models

• Annotating past incidents and system performance data
• Selecting and training algorithms (e.g., LSTM, Random Forest, AutoML)
• Assessing model accuracy and managing false positives

Data Aggregation and Feature Engineering

• Processing and synchronizing log and metric streams for model ingestion
• Extracting meaningful features from both structured and unstructured datasets
• Mitigating noise and addressing missing values in operational data streams

Streamlining Root Cause Analysis (RCA)

• Mapping correlations across services and infrastructure via graph structures
• Utilizing ML to deduce likely root causes from event sequences
• Presenting RCA insights through topology-aware visual dashboards

Remediation and Process Automation

• Connecting with automation frameworks (e.g., Ansible, Rundeck)
• Executing automated rollbacks, restarts, or traffic routing adjustments
• Tracking and recording automated system interventions

Scaling Smart AIOps Architectures

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

Case Studies and Real-World Applications

• Examining actual incident data with predictive AIOps models
• Implementing RCA pipelines using both synthetic and live production data
• Analysis of industry scenarios: cloud service failures, microservice instability, and network performance issues

Conclusion and Future Directions