ML Model Training Component for Network Analytics

The patent divides the monolithic machine learning model into multiple specialized models, with each model trained and optimized for a specific network slice or network side target. This segmentation allows each model to focus on the specific characteristics and requirements of its target slice, thereby improving measurement precision while managing complexity through modular architecture where models are independently trained and can be selectively deployed.

The patent implements local quality by training different machine learning models with different characteristics for different network slices. Each model is optimized for the specific analytics needs of its target slice, such as different feature sets, loss functions, or data processing approaches, allowing each local region (network slice) to receive tailored analytics quality appropriate to its specific requirements.

The patent creates a universal machine learning model management system that can handle multiple specialized models. The system includes a centralized management component that can train, deploy, monitor, and update various models for different network slices using common infrastructure and procedures. This multi-functional approach allows the system to manage model diversity without proportionally increasing complexity, as the same management framework serves all models.

The patent utilizes parameter changes in the model selection and training processes to adapt to different network slice requirements. The system can adjust model parameters such as training data scope, feature engineering approaches, performance metrics, and update frequencies based on the specific needs of each network slice, allowing precise analytics while managing complexity through parameterized configuration rather than structural complexity.