Differentiable ML Pipeline

Differentiable machine learning (ML) pipelines aim to make the entire ML process, from data preprocessing to model training, end-to-end differentiable, enabling optimization of the entire pipeline using gradient-based methods. Current research focuses on developing differentiable versions of traditionally non-differentiable steps like data preprocessing and optimization problems, often employing techniques like convex relaxations and diffusion models to ensure gradient accuracy and robustness. This approach promises to automate and improve the efficiency of ML pipeline design, leading to better model performance and reduced reliance on manual expert intervention across diverse applications, including robotics, natural language processing, and computer vision.