Machine Learning Pipeline

A machine learning pipeline automates the process of building and deploying machine learning models, aiming to improve efficiency and reliability. Current research emphasizes optimizing pipeline components, including data preprocessing (accelerated via hardware and novel algorithms), model selection (leveraging AutoML and knowledge graphs), and bias mitigation (through ontology-based documentation and responsible design patterns). These advancements enhance the reproducibility, interpretability, and trustworthiness of machine learning systems, impacting diverse fields from biomedicine and finance to personalized healthcare and industrial applications.