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Google Professional Machine Learning Engineer Exam - Topic 3 Question 115 Discussion
Topic #: 3
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 requirements and decided to use TensorFlow to build the classifier so that you have full control of the model's code, serving, and deployment. You will use Kubeflow pipelines for the ML platform. To save time, you want to build on existing resources and use managed services instead of building a completely new model. How should you build the classifier?
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.
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
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