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

Introduction to Predictive AIOps

• Overview of predictive analytics in IT operations.
• Data sources for prediction, including logs, metrics, and events.
• Core concepts in time-series forecasting and identifying anomaly patterns.

Creating Incident Prediction Models

• Labeling historical incidents and system behaviors.
• Selecting and training models such as LSTM, Random Forest, or AutoML.
• Assessing model performance and managing false positives.

Data Collection and Feature Engineering

• Ingesting and aligning log and metric data for model input.
• Extracting features from both structured and unstructured data.
• Addressing noise and missing data within operational pipelines.

Automating Root Cause Analysis (RCA)

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

Remediation and Workflow Automation

• Integration with automation platforms like Ansible or Rundeck.
• Initiating rollbacks, restarts, or traffic redirection.
• Auditing and documenting automated interventions.

Scaling Intelligent AIOps Pipelines

• MLOps for observability, including retraining and model versioning.
• Executing predictions in real-time across distributed nodes.
• Best practices for deploying AIOps in production environments.

Case Studies and Practical Applications

• Analyzing real incident data with predictive AIOps models.
• Deploying RCA pipelines using synthetic and production data.
• Reviewing industry use cases such as cloud outages, microservices instability, and network degradations.

Conclusion and Future Steps