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

Introduction to Predictive AIOps

• The role of predictive analytics in modern IT operations.
• Key data sources for prediction, including logs, metrics, and events.
• Foundational concepts in time-series forecasting and identifying anomaly patterns.

Designing Incident Prediction Models

• Annotating historical incident data and system behaviors.
• Selecting and training appropriate models (e.g., LSTM, Random Forest, AutoML).
• Assessing model accuracy and managing false positives.

Data Collection and Feature Engineering

• Ingesting and synchronizing log and metric data for model consumption.
• Extracting meaningful features from both structured and unstructured data.
• Mitigating noise and handling missing data in operational workflows.

Automating Root Cause Analysis (RCA)

• Applying graph-based correlation to services and infrastructure components.
• Leveraging ML to deduce probable root causes from event sequences.
• Visualizing RCA insights through topology-aware dashboards.

Remediation and Workflow Automation

• Connecting with automation tools such as Ansible and Rundeck.
• Executing automated rollbacks, restarts, or traffic rerouting.
• Auditing and documenting automated system interventions.

Scaling Intelligent AIOps Pipelines

• Implementing MLOps for observability, including retraining and version control.
• Running real-time predictions across distributed nodes.
• Best practices for AIOps deployment in production settings.

Case Studies and Practical Applications

• Applying predictive AIOps models to analyze real-world incident data.
• Deploying RCA pipelines using both synthetic and production datasets.
• Examining industry scenarios: cloud outages, microservice instability, and network issues.

Summary and Next Steps