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课程大纲
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
- Choosing and training models (e.g., LSTM, Random Forest, AutoML)
- Evaluating model performance and false-positive handling
Data Collection and Feature Engineering
- Ingesting and aligning log and metric data for model input
- Feature extraction from structured and unstructured data
- Handling 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
要求
- Experience with monitoring systems such as Prometheus or ELK
- Working knowledge of Python and basic machine learning
- Familiarity with incident management workflows
Audience
- Senior site reliability engineers (SREs)
- IT automation architects
- DevOps and observability platform leads
14 小时
