Hardware-Networking
AI for Network Monitoring and Troubleshooting
This practical course helps professionals master AI for network monitoring, anomaly detection, root cause support, ticket enrichment, and troubleshooting workflows. The program connects key concepts, real use cases, risks, tools, and operational decisions so participants can apply the learning in their work environment. It can be tailored to the organization’s sector, internal systems, participant maturity, and performance objectives.
Objectives
- Understand the concepts, challenges, and use cases related to AI for network monitoring, anomaly detection, root cause support, ticket enrichment, and troubleshooting workflows.
- Identify the data, systems, processes, and stakeholders required for effective implementation.
- Assess risks, limitations, governance requirements, and practical control points.
- Use methods, tools, and templates to structure analysis and decision-making.
- Translate learning into action plans, recommendations, and measurable improvement opportunities.
- Adapt the approach to the operating context, team maturity, and business objectives.
Target audience
- Network, systems, and infrastructure administrators
- Support technicians and IT engineers
- Data center, endpoint, and operations teams
- Professionals preparing for networking certifications
- IT managers responsible for availability and resilience
Program outline
A clear structure for the learning journey.
Program outline
Outline points are grouped in one designed block instead of being treated as separate module cards.
Module 1: Network Monitoring Data, Telemetry, Logs, and Events
Foundation for Network Monitoring Data, Telemetry, Logs, and Events: application, analysis, and review points linked to the module
Terminology and decisions in Network Monitoring Data, Telemetry, Logs, and Events: application, analysis, and review points linked to the module
Inputs required for Network Monitoring Data, Telemetry, Logs, and Events: application, analysis, and review points linked to the module
Typical mistakes around Network Monitoring Data, Telemetry, Logs, and Events: applied exercise and practical decision from a realistic scenario
Module 2: AI Use Cases for Anomaly Detection and Incident Reduction
Current-state mapping for AI Use Cases for Anomaly Detection and Incident Reduction: application, analysis, and review points linked to the module
Examples and scenarios involving AI Use Cases for Anomaly Detection and Incident Reduction: application, analysis, and review points linked to the module
Diagnostic questions about AI Use Cases for Anomaly Detection and Incident Reduction: application, analysis, and review points linked to the module
Evidence produced through AI Use Cases for Anomaly Detection and Incident Reduction: applied exercise and practical decision from a realistic scenario
Module 3: Baselines, Thresholds, Seasonality, and Noise Reduction
Design considerations for Baselines, Thresholds, Seasonality, and Noise Reduction: application, analysis, and review points linked to the module
Roles and responsibilities in Baselines, Thresholds, Seasonality, and Noise Reduction: application, analysis, and review points linked to the module
Exceptions and constraints affecting Baselines, Thresholds, Seasonality, and Noise Reduction: application, analysis, and review points linked to the module
Quality checks for Baselines, Thresholds, Seasonality, and Noise Reduction: applied exercise and practical decision from a realistic scenario
Module 4: Root Cause Support, Correlation, and Dependency Mapping
Operating model for Root Cause Support, Correlation, and Dependency Mapping: application, analysis, and review points linked to the module
Tools and workflow steps in Root Cause Support, Correlation, and Dependency Mapping: application, analysis, and review points linked to the module
Handoffs and approvals around Root Cause Support, Correlation, and Dependency Mapping: application, analysis, and review points linked to the module
Escalation points in Root Cause Support, Correlation, and Dependency Mapping: applied exercise and practical decision from a realistic scenario
Module 5: Ticket Enrichment, Suggested Actions, and Human Review
Performance measures for Ticket Enrichment, Suggested Actions, and Human Review: application, analysis, and review points linked to the module
Review routines after Ticket Enrichment, Suggested Actions, and Human Review: application, analysis, and review points linked to the module
Improvement actions for Ticket Enrichment, Suggested Actions, and Human Review: application, analysis, and review points linked to the module
Sustaining discipline around Ticket Enrichment, Suggested Actions, and Human Review: applied exercise and practical decision from a realistic scenario
Module 6: Integration with NMS, SIEM, ITSM, and Observability Tools
Advanced scenarios in Integration with NMS, SIEM, ITSM, and Observability Tools: application, analysis, and review points linked to the module
Failure patterns seen in Integration with NMS, SIEM, ITSM, and Observability Tools: application, analysis, and review points linked to the module
Coordination challenges during Integration with NMS, SIEM, ITSM, and Observability Tools: application, analysis, and review points linked to the module
Recovery actions for Integration with NMS, SIEM, ITSM, and Observability Tools: applied exercise and practical decision from a realistic scenario
Module 7: Model Quality, False Positives, Governance, and Adoption
Governance requirements for Model Quality, False Positives, Governance, and Adoption: application, analysis, and review points linked to the module
Data quality checks in Model Quality, False Positives, Governance, and Adoption: application, analysis, and review points linked to the module
Risk controls related to Model Quality, False Positives, Governance, and Adoption: application, analysis, and review points linked to the module
Value measures for Model Quality, False Positives, Governance, and Adoption: applied exercise and practical decision from a realistic scenario
Module 8: AI Network Monitoring Use Case Workshop
Implementation planning for AI Network Monitoring Use Case Workshop: application, analysis, and review points linked to the module
Readiness questions before AI Network Monitoring Use Case Workshop: application, analysis, and review points linked to the module
Pilot design for AI Network Monitoring Use Case Workshop: application, analysis, and review points linked to the module
Lessons learned after AI Network Monitoring Use Case Workshop: applied exercise and practical decision from a realistic scenario
Materials provided
- ○ Slides used during the sessions
- ○ Group activities and practical exercises
- ○ Worksheets, checklists, and templates
- ○ Case studies relevant to the course
- ○ 4D Certificate of Completion issued by 4D Training & Consultancy
- ○ Post-course support for technical queries and guidance
Training Options
Programs can be delivered in-house, online, or in a blended format depending on your team's schedule, location, and learning objectives. When an external certificate or exam is included, certification rules and fees remain under the relevant awarding body's policies, while 4D provides the training and preparation support.
Why choose 4D
4D Training & Consultancy designs technical and professional programs around the client’s operating reality. The course can be adapted to sector requirements, internal systems, team capability, practical use cases, and the level of depth required by the audience.
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