Model Switching

Model switching involves dynamically selecting the most appropriate machine learning model for a given task based on factors like dataset size and characteristics, aiming to optimize performance and resource utilization. Current research focuses on developing adaptive algorithms that seamlessly transition between different models (e.g., CatBoost and XGBoost, or various Vision Transformers) using criteria such as accuracy thresholds or Quality of Service (QoS) metrics. This approach is particularly relevant in resource-constrained environments or when dealing with diverse data streams, offering improvements in efficiency and predictive accuracy across various applications, including medical image diagnosis and general machine learning tasks.