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

Foundations of Predictive AIOps

• The role of predictive analytics in modern IT operations
• Data inputs for prediction models, including logs, metrics, and events
• Core principles of time-series forecasting and anomaly detection

Architecting Incident Prediction Models

• Annotating historical incidents and system behaviors for training
• Selecting and training appropriate 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 consumption
• Extracting meaningful features from both structured and unstructured data sets
• Mitigating noise and handling missing data in operational streams

Streamlining Root Cause Analysis (RCA)

• Applying graph-based methods to correlate services and infrastructure components
• Utilizing ML to deduce probable root causes from sequential event chains
• Visualizing RCA insights through topology-aware dashboards

Automating Remediation and Workflows

• Connecting with automation frameworks (e.g., Ansible, Rundeck)
• Initiating automated rollbacks, restarts, or traffic rerouting
• Maintaining audit trails and documenting automated interventions

Scaling Intelligent AIOps Pipelines

• MLOps for observability: model retraining and version control strategies
• Executing real-time predictions across distributed computing nodes
• Best practices for production deployment of AIOps solutions

Real-World Case Studies and Applications

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

Recap and Future Directions