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

Introduction to Predictive AIOps

• The role of predictive analytics in modern IT operations.
• Identifying data sources for prediction, including logs, metrics, and events.
• Core principles of time-series forecasting and recognizing anomaly patterns.

Creating Incident Prediction Models

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

Data Acquisition and Feature Engineering

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

Streamlining Root Cause Analysis (RCA)

• Utilizing graph-based methods to correlate services and infrastructure components.
• Applying ML to deduce likely root causes from event sequences.
• Presenting RCA insights through topology-aware dashboards.

Remediation and Workflow Automation

• Connecting with automation frameworks (e.g., Ansible, Rundeck).
• Initiating rollbacks, service restarts, or traffic shifting.
• Monitoring and recording automated corrective actions.

Expanding Intelligent AIOps Pipelines

• MLOps for observability: strategies for retraining and versioning models.
• Executing predictions in real-time across distributed systems.
• Best practices for rolling out AIOps in production.

Case Studies and Real-World Applications

• Examining actual incident data using predictive AIOps techniques.
• Implementing RCA pipelines with both synthetic and live production data.
• Reviewing industry examples, including cloud outages, microservice instability, and network performance drops.

Recap and Future Directions