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?
To build an ML solution for classifying support requests using TensorFlow in Google Cloud, while having full control of the model's code, serving, and deployment, the best approach is to use an established text classification model on AI Platform to perform transfer learning. Using transfer learning allows you to leverage an existing model's capabilities and adapt it to your specific needs by retraining it with your data. This method saves time by building on existing resources and managed services without having to create a completely new model from scratch.
ANS: C as you want to have full control of the model code.
ANS: D https://cloud.google.com/ai-platform/training/docs/algorithms - to use TensorFlow - to build on existing resources - to use managed services
While D is very close for me, I think there are 2 giveaways here: "To save time, you want to build on existing resources" - transfer learning "instead of building a completely new model" - answer D leaves the model as is ANS C:
the model cannot work as-is as the classes to predict will likely not be the same; we need to use transfer learning to retrain the last layer and adapt it to the classes we need, hence C
"full control of the model's code, serving, and deployment": Not A nor B. and "you want to build on existing resources and use managed services": Not D (that's "as-is") You want transfer learning.
Went with C
- "You analyzed the requirements and decided to use TensorFlow" this will make choices to reduce to C and D - " so that you have full control of the model's code " will make us choose C
ANS: C according to me as well. As arbik said, full control, custom model are give aways.
Cis correct
According to me it is B. A is not correct as it uses an API call only and we won't build the system on existing resources. C & D I do not see in AI Platform (Vertex AI) an established text classification that can be used. The B answer is the right one, you have the labeled data, you need to remove the custom TF code and build a classifier with AutoML Natural Language
B is wrong. question says " you have full control of the model's code". You don't have full control of automl code. The right answer is C.
Answer is C.
you don't need transfer learning in this case
Usage of Tensorflow, can build a simple model by using a sentence embedding and a single layer classifier.
C) Transfer learning