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
Applying network monitoring data, telemetry, logs, and events in the context of AI for network monitoring, anomaly detection, root cause support, ticket enrichment, and troubleshooting workflows
Practical exercises, control points, deliverables, and related decisions
Module 2: AI Use Cases for Anomaly Detection and Incident Reduction
Applying ai use cases for anomaly detection and incident reduction in the context of AI for network monitoring, anomaly detection, root cause support, ticket enrichment, and troubleshooting workflows
Practical exercises, control points, deliverables, and related decisions
Module 3: Baselines, Thresholds, Seasonality, and Noise Reduction
Applying baselines, thresholds, seasonality, and noise reduction in the context of AI for network monitoring, anomaly detection, root cause support, ticket enrichment, and troubleshooting workflows
Practical exercises, control points, deliverables, and related decisions
Module 4: Root Cause Support, Correlation, and Dependency Mapping
Applying root cause support, correlation, and dependency mapping in the context of AI for network monitoring, anomaly detection, root cause support, ticket enrichment, and troubleshooting workflows
Practical exercises, control points, deliverables, and related decisions
Module 5: Ticket Enrichment, Suggested Actions, and Human Review
Applying ticket enrichment, suggested actions, and human review in the context of AI for network monitoring, anomaly detection, root cause support, ticket enrichment, and troubleshooting workflows
Practical exercises, control points, deliverables, and related decisions
Module 6: Integration with NMS, SIEM, ITSM, and Observability Tools
Applying integration with nms, siem, itsm, and observability tools in the context of AI for network monitoring, anomaly detection, root cause support, ticket enrichment, and troubleshooting workflows
Practical exercises, control points, deliverables, and related decisions
Module 7: Model Quality, False Positives, Governance, and Adoption
Applying model quality, false positives, governance, and adoption in the context of AI for network monitoring, anomaly detection, root cause support, ticket enrichment, and troubleshooting workflows
Practical exercises, control points, deliverables, and related decisions
Module 8: AI Network Monitoring Use Case Workshop
Applying ai network monitoring use case workshop in the context of AI for network monitoring, anomaly detection, root cause support, ticket enrichment, and troubleshooting workflows
Practical exercises, control points, deliverables, and related decisions
Materials provided
- â—‹ Slides used during the sessions
- â—‹ Group activities and practical exercises
- â—‹ Worksheets, checklists, and templates
- â—‹ Case studies relevant to the course
- â—‹ 4D Certificate of Completion issued by 4D Training & Consultancy
- â—‹ 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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