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

Foundations of Predictive AIOps

• Insights into predictive analytics within IT operations
• Identifying data streams for forecasting (logs, metrics, events)
• Core principles of time-series forecasting and anomaly detection

Architecting Incident Prediction Models

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

Data Acquisition and Feature Engineering

• Processing and synchronizing log and metric data for model consumption
• Extracting meaningful features from both structured and unstructured data
• Managing noise and missing values in operational data flows

Streamlining Root Cause Analysis (RCA)

• Applying graph-based correlation to map services and infrastructure
• Leveraging ML to deduce likely root causes from event sequences
• Presenting RCA insights via topology-aware visualizations

Remediation and Process Automation

• Connecting with automation frameworks (e.g., Ansible, Rundeck)
• Initiating rollbacks, service restarts, or traffic rerouting
• Logging and auditing automated corrective actions

Scaling Intelligent AIOps Pipelines

• MLOps for observability: managing retraining and model versions
• Executing real-time predictions across distributed systems
• Strategic guidelines for deploying AIOps in production landscapes

Case Studies and Real-World Applications

• Applying predictive AIOps models to analyze actual incident data
• Implementing RCA pipelines using both synthetic and live data
• Exploring industry scenarios: cloud failures, microservice instability, and network issues

Wrap-Up and Future Directions