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

Foundamentals of Predictive AIOps

• An overview of predictive analytics within IT operations.
• Exploring data sources used for prediction, including logs, metrics, and events.
• Core concepts related to time-series forecasting and anomaly detection patterns.

Architecting Incident Prediction Models

• Labeling past incidents and system behaviors for training purposes.
• Selecting and training appropriate models (such as LSTM, Random Forest, or AutoML).
• Assessing model accuracy and managing false-positive detections.

Data Gathering and Feature Engineering

• Ingesting and aligning log and metric data to serve as model inputs.
• Extracting meaningful features from both structured and unstructured datasets.
• Addressing noise and missing data issues within operational pipelines.

Streamlining Root Cause Analysis (RCA)

• Utilizing graph-based methods to correlate services and infrastructure components.
• Applying machine learning to deduce probable root causes from event sequences.
• Visualizing RCA results through topology-aware dashboard interfaces.

Automation of Remediation and Workflows

• Integrating with established automation platforms like Ansible and Rundeck.
• Executing automated actions such as rollbacks, service restarts, or traffic redirection.
• Maintaining audit trails and documenting automated interventions.

Scaling Intelligent AIOps Pipelines

• Applying MLOps principles to observability, including model retraining and version control.
• Enabling real-time prediction across distributed computing nodes.
• Best practices for deploying AIOps solutions in live production environments.

Case Studies and Real-World Applications

• Analyzing actual incident data using predictive AIOps models.
• Implementing RCA pipelines using both synthetic and live production data.
• Reviewing industry-specific use cases, including cloud outages, microservice instability, and network performance issues.

Conclusion and Path Forward