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

Introduction to Predictive AIOps

• An overview of predictive analytics within IT operations.
• Key concepts in time-series forecasting and recognizing anomaly patterns.

Designing Incident Prediction Models

• Labeling past incidents and associated 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 consumption.
• Extracting features from both structured and unstructured datasets.
• Addressing noise and missing data within operational pipelines.

Automating Root Cause Analysis (RCA)

• Graph-based correlation of services and infrastructure components.
• Utilizing ML to deduce probable root causes from event sequences.
• Visualizing RCA insights using topology-aware dashboards.

Remediation and Workflow Automation

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

Scaling Intelligent AIOps Pipelines

• MLOps for observability: covering 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

• Applying predictive AIOps models to analyze real-world incident data.
• Implementing RCA pipelines using both synthetic and production data.
• Reviewing industry use cases: cloud outages, microservice instability, and network degradations.

Summary and Next Steps