Multi Model

Multi-model approaches in machine learning aim to improve performance and efficiency by combining multiple models or leveraging diverse data sources. Current research focuses on optimizing multi-model inference pipelines for speed and cost-effectiveness, exploring heterogeneous architectures like multi-chip modules and employing techniques such as mixture of experts and reinforcement learning for improved model alignment and adaptation. These advancements are impacting various fields, from improving the accuracy of medical image analysis and environmental modeling to enhancing the efficiency of large language model deployment and real-time applications in extended reality.