Transfer learning is a technique that leverages the knowledge and weights of a pre-trained model and adapts them to a new task or domain 1 .  Transfer learning can save time and resources by avoiding training a model from scratch, and can also improve the performance and generalization of the model by using a larger and more diverse dataset 2 .  AI Platform provides several established text classification models that can be used for transfer learning, such as BERT, ALBERT, or XLNet 3 .  These models are based on state-of-the-art natural language p rocessing techniques and can handle various text classification tasks, such as sentiment analysis, topic classification, or spam detection 4 . By using one of these models on AI Platform, you can customize the model’s code, serving, and deployment, and use Kubeflow pipelines for the ML platform. Therefore, using an established text classification model on AI Platform to perform transfer learning is the best option for this use case.