Disadvantages of Labelled Data
In the machine learning lifecycle, labelled data is often regarded as gold standard—critical for training supervised learning models. However, obtaining and using labelled data comes with notable downsides. From high annotation costs to inherent biases and scalability issues, relying heavily on labelled datasets can constrain the development and deployment of AI systems. In this comprehensive … Read more