AWS Certified Machine Learning Specialty (MLS-C01) Practice Test

Transfer learning is particularly advantageous when adapting a pre-trained model to a related task because it leverages the knowledge that the model has already gained from being trained on a large dataset. This allows for faster training and often leads to improved performance, especially when the new task does not have a large amount of labeled data available. By starting with an existing model, you can utilize the learned representations and features that are likely to be relevant to the new task, even if the specific data or labels differ.

In cases of having an abundance of labeled data, building a model from scratch may often yield better performance since you can tailor the model specifically to the nuances of the new dataset without the biases that a pre-trained model might carry. Similarly, when there is no available data for the new task, transfer learning becomes ineffective because there is no knowledge or features to adapt from the pre-trained model. Lastly, building a model from scratch when there is no prior knowledge provides the opportunity to create a unique architecture for a specific problem, but it does not benefit from the efficiencies and capabilities gained from transfer learning.