Weak supervision is a machine learning paradigm that utilizes imperfect, limited, or noisy labels to train models when fully annotated datasets are unavailable or too costly to obtain. This approach can include various techniques such as label propagation, semi-supervised learning, and using heuristics to generate labels. By leveraging weak supervision, practitioners can create models that still perform well despite the quality of the training data. This technique is particularly useful in domains where obtaining high-quality labels is challenging, allowing for more scalable and efficient training processes while still achieving reasonable accuracy.