Machine Learning Challenges

Machine Learning Challenges refer to various obstacles and difficulties encountered in the development, implementation, and deployment of machine learning models. These challenges can arise at different stages, including data collection, preprocessing, model selection, training, evaluation, and operationalization. Common issues include dealing with insufficient or unbalanced datasets, overfitting, underfitting, computational resource limitations, algorithmic complexity, and the need for interpretability and explainability in model predictions. Additionally, challenges may involve adapting to dynamic environments where models must continually learn from new data or improving the robustness of models against adversarial attacks. Addressing these challenges is crucial for effectively leveraging machine learning technologies in practical applications.