Network Analytics and Artificial Intelligence (AI) Training
- Duration: 24 Hours
- Exam Voucher: Yes
- Language: English
- Course Delivery : E - Learning Access
9000+ Professionals Enrolled
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Course Overview
The AI for Network Engineers Course delivers essential training for networking professionals looking to understand how artificial intelligence transforms modern network operations. Participants gain a structured understanding of how AI and machine learning analyze telemetry and operational data to provide visibility, prediction, and automation across enterprise and service provider networks. The course emphasizes practical interpretation of analytics, recognizing patterns, and using AI-assisted insights to reduce downtime and accelerate troubleshooting. Guided labs and examples reinforce how AI augments traditional network engineering workflows.
Course Details
- Build foundational understanding of AI and machine learning concepts for networking.
- Learn how telemetry and data analytics drive AI-powered network insights.
- Understand anomaly detection and predictive analytics use cases.
- Gain exposure to AI-assisted monitoring and troubleshooting workflows.
- Improve operational efficiency through data-driven decision-making.
- Establish a strong foundation for advanced AI, analytics, and automation pathways.
- Ideal for network engineers, operations teams, and IT professionals.
- Recommended to have foundational knowledge of networking concepts.
- No prior AI or data science experience required.
- No prerequisite certifications required for course participation.
- Explain core AI and machine learning concepts relevant to networking.
- Understand how network telemetry is collected and analyzed.
- Interpret AI-driven insights for network health and performance.
- Identify anomalies and trends using analytics tools.
- Apply AI-assisted approaches to troubleshooting and operations.
- Prepare for advanced training in network analytics and AI-driven networking.
- AI Fundamentals for Network Engineers: Concepts, terminology, and use cases.
- Telemetry and Data Collection: Metrics, events, and data sources.
- Machine Learning Basics: Supervised and unsupervised learning awareness.
- Anomaly Detection: Identifying abnormal network behavior.
- Predictive Analytics: Anticipating issues and performance trends.
- AI-Assisted Troubleshooting: Faster fault isolation and resolution.
- Operational Best Practices: Integrating AI into daily network operations.
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