Evaluating ML Forecasts in Hybrid Network

Introduction:
 The concept of hybrid networks that combine cloud and edge computing for efficient machine learning forecasting.

Hybrid Network Architecture Overview:
Explains how edge and cloud systems integrate to support distributed and scalable forecasting operations.

Role of Machine Learning in Forecasting:
Describes how ML models predict patterns, demands, or events using dynamic network data.

Connectivity Challenges and Data Flow:
Examines how unstable connectivity impacts data transmission, synchronization, and overall forecast accuracy.

Benchmarking Metrics and Evaluation Criteria:
Defines accuracy, latency, scalability, and fault tolerance as essential performance indicators.

Impact of Edge vs. Cloud Processing:
Compares forecasting speed and reliability between local edge execution and centralized cloud processing.

Model Optimization for Mixed Connectivity:
Discusses adaptive approaches that enhance model performance under varying connectivity conditions.

Case Studies and Simulation Results:
Highlights experimental results demonstrating ML forecasting effectiveness in hybrid environments.

Conclusion:
Concludes that optimizing ML forecasting models for hybrid networks ensures greater reliability, efficiency, and real-time adaptability.