Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
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
- 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
Requirements
- 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 Hours
