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Course Outline
Introduction to Predictive AIOps
- Overview of predictive analytics in IT operations.
- Data sources for prediction (logs, metrics, events).
- Key concepts in time-series forecasting and anomaly patterns.
Designing Incident Prediction Models
- Labeling historical incidents and system behavior.
- Selecting and training models (e.g., LSTM, Random Forest, AutoML).
- Evaluating model performance and handling false positives.
Data Collection and Feature Engineering
- Ingesting and aligning log and metric data for model input.
- Extracting features from structured and unstructured data.
- Managing noise and missing data in operational pipelines.
Automating Root Cause Analysis (RCA)
- Graph-based correlation of services and infrastructure.
- Using ML to infer probable root causes from event chains.
- Visualizing RCA with topology-aware dashboards.
Remediation and Workflow Automation
- Integrating with automation platforms (e.g., Ansible, Rundeck).
- Triggering rollbacks, restarts, or traffic redirection.
- Auditing and documenting automated interventions.
Scaling Intelligent AIOps Pipelines
- MLOps for observability: retraining and model versioning.
- Running predictions in real-time across distributed nodes.
- Best practices for deploying AIOps in production environments.
Case Studies and Practical Applications
- Analyzing real incident data using predictive AIOps models.
- Deploying RCA pipelines with synthetic and production data.
- Review of industry use cases: cloud outages, microservices instability, network degradations.
Summary and Next Steps
Requirements
- Experience with monitoring systems such as Prometheus or ELK.
- Practical knowledge of Python and basic machine learning concepts.
- Familiarity with incident management workflows.
Target Audience
- Senior Site Reliability Engineers (SREs).
- IT Automation Architects.
- DevOps and Observability Platform Leads.
14 Hours
