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?
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?
To minimize computational overhead for batch predictions on text data stored in BigQuery, the best option is to submit a batch prediction job on AI Platform that points to the model location in Cloud Storage. This method allows for direct integration with the trained model and efficient processing of large datasets, leveraging the capabilities of AI Platform for scalable and optimized predictions. Exporting the model to BigQuery ML, on the other hand, is not suitable for text classification models as BigQuery ML mainly supports structured data and simpler machine learning models.
A. You would want to minimize computational overhead–BigQuery minimizes such overhead
BQML doesnt support NLP model
you can import a TF model in BQ ML
agree. https://cloud.google.com/bigquery-ml/docs/making-predictions-with-imported-tensorflow-models
No need . This is a text classification problem. need to convert words to numbers and use a classifier.
I think it's A https://cloud.google.com/bigquery-ml/docs/making-predictions-with-imported-tensorflow-models#importing_models
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.
I think D have extra compute on extrating data frm BQ
minimize computational overhead–>BigQuery
Model : AI Platform. pred batch data : BigQuery constraint : computational overhead Same platform as data == less computation required to load and pass it to model
A - you can import TF models to BQ
Bquery to minimize computational overhead
D is more straightforward
what about C?
This is an option that can be used to minimize computational overhead, but it is more complex to set up and requires you to have Dataflow installed.
Although it's more complex, the question doesn't imply any restrictions on complexity, only computational overheard
why not C?
Went with D
Not sure Text Classification Using BigQuery ML and ML.NGRAMS https://medium.com/@jeffrey.james/text-classification-using-bigquery-ml-and-ml-ngrams-6e365f0b5505
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
Would go with D
Use the gcloud command to submit a batch prediction job, specifying the model location in Cloud Storage and the BigQuery table as the input source.
A) BigQuery ML