Your team has been tasked with creating an ML solution in Google Cloud to classify support requests for one of your platforms. You analyzed the…

Transfer learning is a technique that leverages the knowledge and weights of a pre-trained model

and adapts them to a new task or domain1. 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 dataset2. AI Platform provides several established text

classification models that can be used for transfer learning, such as BERT, ALBERT, or XLNet3. These

models are based on state-of-the-art natural language processing techniques and can handle various

text classification tasks, such as sentiment analysis, topic classification, or spam detection4. 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.

Reference:

Transfer Learning - Machine Learning’s Next Frontier

A Comprehensive Hands-on Guide to Transfer Learning with Real-World Applications in Deep

Learning

Text classification models

Text Classification with Pre-trained Models in TensorFlow