Machine Learning Project

Machine learning (ML) project research currently focuses on improving the efficiency and reliability of the entire ML lifecycle, from initial data acquisition and model selection to deployment and maintenance. Key areas of investigation include optimizing continuous integration practices, enhancing data and model management, and developing tools to improve code quality and address biases. This work aims to streamline ML development, improve reproducibility, and ultimately increase the trustworthiness and impact of ML applications across various domains.