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

Delivery Options

Private Group Training

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  • Delivered online, onsite/classroom or hybrid by experts sharing real world experience

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