End to End Pipeline

End-to-end pipelines automate complex workflows in various scientific domains, streamlining data processing and analysis from raw input to final results. Current research emphasizes the application of deep learning models, such as convolutional neural networks and large language models, within these pipelines, often coupled with novel loss functions or data augmentation techniques to improve accuracy and efficiency. These advancements are significantly impacting fields ranging from medical image analysis and biological data processing to environmental monitoring and social media bias mitigation, enabling faster, more accurate, and accessible scientific discovery and practical applications. The focus is on improving both the accuracy and efficiency of these pipelines, often by addressing specific challenges like bias mitigation or limited training data.