You have trained a text classification model in TensorFlow using AI Platform. You want to use the trained model for batch predictions on text data stored in BigQuery while minimizing computational overhead. What should you do?
A.
Export the model to BigQuery ML.
B.
Deploy and version the model on AI Platform.
C.
Use Dataflow with the SavedModel to read the data from BigQuery.
D.
Submit a batch prediction job on AI Platform that points to the model location in Cloud Storage.
Not option A because BigQuery ML can be useful for certain tasks, it might not be the most efficient for batch predictions with a custom TensorFlow model trained on AI Platform.
A is incorrect:
BigQuery ML is used to train and deploy models directly within BigQuery, but it does not support importing and deploying external TensorFlow models.
You cannot export a TensorFlow model directly to BigQuery ML; AI Platform is the correct service for TensorFlow-based models.
However BQ ML requires storing to Cloud Storage first. the question doesn't state this (should we assume?), which make Answer D better as it state cloud storage.
Model : AI Platform.
pred batch data : BigQuery
constraint : computational overhead
Same platform as data == less computation required to load and pass it to model
Not sure if when you have the saved model in Cloud storage that means that you don't use compute in vertex. I think that the option compute-free is bigquery
Not sure
Text Classification Using BigQuery ML and ML.NGRAMS
https://medium.com/@jeffrey.james/text-classification-using-bigquery-ml-and-ml-ngrams-6e365f0b5505
There are some drawbacks to option D.
Cost: Submitting a batch prediction job on AI Platform is a paid service. The cost will depend on the size of the model and the amount of data that you are predicting.
Complexity: Submitting a batch prediction job on AI Platform requires you to write some code. This can be a challenge if you are not familiar with AI Platform.
Performance: Submitting a batch prediction job on AI Platform may not be as efficient as using BigQuery ML. This is because AI Platform needs to load the model into memory before it can run the predictions.
Overall, option D is a viable option, but it may not be the best option for all situations.
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