Machine Learning Workflow

Machine learning workflows encompass the entire process of developing, deploying, and maintaining machine learning models, aiming to optimize efficiency, reproducibility, and performance across diverse applications. Current research emphasizes automating workflow management, improving model verification and validation through techniques like interpolation error bounds, and enhancing data quality through active learning and data-centric approaches. These advancements are crucial for addressing challenges in scalability, interpretability, and trustworthiness, ultimately accelerating scientific discovery and enabling more robust and reliable AI-driven systems in various fields.