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Course Outline
Introduction to Predictive AIOps
- Overview of predictive analytics in IT operations.
- Data sources for prediction, including logs, metrics, and events.
- Key concepts in time-series forecasting and anomaly detection.
Designing Incident Prediction Models
- Labeling historical incidents and system behavior for training.
- Selecting and training models (e.g., LSTM, Random Forest, AutoML).
- Evaluating model performance and managing false positives.
Data Collection and Feature Engineering
- Ingesting and aligning log and metric data for model inputs.
- Extracting features from both structured and unstructured data.
- Addressing noise and missing data in operational pipelines.
Automating Root Cause Analysis (RCA)
- Correlating services and infrastructure using graph-based methods.
- Leveraging ML to infer probable root causes from event chains.
- Visualizing RCA outcomes with topology-aware dashboards.
Remediation and Workflow Automation
- Integrating with automation platforms such as Ansible or Rundeck.
- Triggering rollbacks, service restarts, or traffic redirections.
- Auditing and documenting automated interventions.
Scaling Intelligent AIOps Pipelines
- Applying MLOps for observability, including model retraining and versioning.
- Executing real-time predictions across distributed nodes.
- Adhering to best practices for deploying AIOps in production.
Case Studies and Practical Applications
- Analyzing real incident data using predictive AIOps models.
- Deploying RCA pipelines with both synthetic and production data.
- Reviewing industry use cases: cloud outages, microservices instability, and network degradations.
Summary and Next Steps
Requirements
- Experience with monitoring systems like Prometheus or ELK.
- Working 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
