Using AI to Manage Networking Devices in Production

Part 1: Introduction to AI in Networking
• Module 1: Fundamentals of AI and ML for Network Management
o Overview of AI, ML, and data science in networking
o Common AI applications in network monitoring and management
o Introduction to relevant data sources and types in networking (logs, SNMP, flow data, 
etc.)
o Hands-On Activity: Setting up a basic AI environment for network data analysis

• Module 2: Data Collection and Preprocessing
o Understanding network data requirements for AI
o Techniques for data collection from devices, servers, and monitoring tools
o Data cleaning, preprocessing, and feature engineering
o Hands-on Lab: Collecting and preprocessing network data for analysis

• Module 3: Exploring AI Use Cases in Networking
o Case studies: AI in network traffic analysis, performance optimization, and security
o Identifying and assessing AI opportunities within existing network infrastructures
o Group Activity: Brainstorming AI use cases for participants’ own networks

Part 2: Anomaly Detection and Fault Management
• Module 1: Introduction to Anomaly Detection in Networks
o Types of anomalies in network traffic and device performance
o Overview of ML models for anomaly detection (e.g., clustering, time-series analysis)
o Hands-On Activity: Exploring and visualizing network data for anomaly detection
• Module 2: Building and Training Anomaly Detection Models
o Selecting and training machine learning models for real-time anomaly detection
o Model evaluation metrics for network monitoring (e.g., precision, recall)
o Hands-on Lab: Developing a basic anomaly detection model for a network dataset
• Module 3: Automated Fault Detection and Alerts
o Setting thresholds, triggers, and alerts using ML models
o Integrating anomaly detection models with alerting systems
o Hands-On Activity: Deploying an anomaly detection model with automated alerting

Part 3: Predictive Maintenance and Optimization of Network Performance
• Module 1: Predictive Maintenance Basics and Use Cases
o Understanding predictive maintenance and its impact on network reliability
o Reviewing ML models used for predictive maintenance in networking
o Hands-On Activity: Creating datasets for predictive maintenance from network logs
• Module 2: Developing Predictive Maintenance Models
o Feature selection and engineering for predicting device failure and degradation
o Training predictive models and evaluating performance
o Hands-on Lab: Building and deploying a predictive maintenance model
• Module 3: AI for Network Performance Optimization
o Using AI to optimize network traffic, load balancing, and resource allocation
o Overview of reinforcement learning in dynamic network management
o Hands-On Activity: Implementing a basic traffic optimization model

Part 4: Automating Network Management and Security with AI
• Module 1: Automated Network Configuration and Management
o Tools and frameworks for AI-driven network configuration (e.g., SDN with AI)
o Scripting and automation for real-time network adjustments
o Hands-on Lab: Creating scripts for automated configuration adjustments
• Module 2: Enhancing Network Security with AI
o Overview of AI techniques for intrusion detection and threat prediction
o Implementing ML models for identifying security risks and responding in real-time
o Hands-On Activity: Developing and deploying a basic intrusion detection model
• Module 3: Evaluating and Scaling AI Solutions in Networking
o Best practices for evaluating model performance and effectiveness
o Considerations for scaling AI solutions in enterprise networks