Scalable ML System for Real-Time Network Monitoring

Introduction: Explains the need for scalable machine learning to handle real-time network data and performance insights.

Problem Statement: Highlights rising network complexity and the limitations of traditional monitoring tools.

Objectives: Defines the goal of building an adaptive ML system for accurate, real-time analytics.

Big Data Integration: Describes how large-scale network traffic is collected, processed, and streamed into the ML pipeline.

ML Model Design: Outlines the lightweight, scalable ML models used for anomaly detection and traffic prediction.

Real-Time Processing: Explains how the system uses streaming frameworks for low-latency monitoring.

System Architecture: Summarizes the distributed architecture enabling high performance and horizontal scalability.

Evaluation & Results: Presents validation results showing improved detection speed and system efficiency.

Conclusion: Concludes that scalable ML significantly enhances real-time network monitoring and operational reliability.