** Active learning is a supervised machine learning approach that aims to optimize annotation using a few small training samples. It helps overcome the challenges of annotating large datasets, which can be costly and time-consuming. Active learning pipelines and algorithms select data points for labeling based on their informativeness, reducing the amount of labeled data required while improving model performance. The process involves iteratively selecting the most informative samples to label, incorporating them into the training set, and updating the model until a stopping criterion is met or further improvements are not possible. Active learning can be applied in various domains, including computer vision, natural language processing, image classification, and more, where obtaining labeled data can be challenging. By strategically selecting which data points to label, active learning improves the accuracy and generalization of machine learning models, reducing labeling costs and improving performance.