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

Introduction to Predictive AIOps

• Role of predictive analytics in modern IT operations.
• Identifying data sources for forecasting (logs, metrics, events).
• Core principles of time-series forecasting and anomaly detection.

Architecting Incident Prediction Models

• Annotating 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

• Processing and aligning log and metric data for model ingestion.
• Extracting features from both structured and unstructured data sets.
• Managing noise and data gaps in operational workflows.

Streamlining Root Cause Analysis (RCA)

• Graph-based mapping of service and infrastructure dependencies.
• Leveraging ML to deduce likely root causes from event sequences.
• Visualizing RCA results via topology-aware dashboards.

Remediation and Workflow Automation

• Connecting with automation frameworks (e.g., Ansible, Rundeck).
• Automating rollbacks, restarts, or traffic rerouting.
• Maintaining audit trails and documentation for automated actions.

Scaling Intelligent AIOps Pipelines

• Applying MLOps to observability: model retraining and version control.
• Executing real-time predictions across distributed infrastructure.
• Best practices for deploying AIOps in live production environments.

Real-World Case Studies and Applications

• Interpreting actual incident data using predictive AIOps models.
• Implementing RCA pipelines using both synthetic and live data.
• Examining industry scenarios: cloud service outages, microservice instability, and network performance degradation.

Conclusion and Forward Path