Machine Learning Practitioner

Machine learning (ML) practitioners are increasingly focused on developing robust, responsible, and efficient ML systems. Current research emphasizes improving the entire ML lifecycle, from data acquisition and preprocessing (including addressing bias and missing data) to model training, evaluation, deployment, and ongoing monitoring. This involves developing new tools and frameworks to enhance collaboration, transparency, and explainability, particularly through interactive interfaces and knowledge graph-based approaches. The ultimate goal is to improve the reliability, fairness, and societal impact of ML applications across diverse domains.