Exam Professional Machine Learning Engineer topic 1 question 38 discussion

A. You would want to minimize computational overhead–BigQuery minimizes such overhead

I think it's A https://cloud.google.com/bigquery-ml/docs/making-predictions-with-imported-tensorflow-models#importing_models

A) BigQuery ML

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.

Bquery to minimize computational overhead

Would go with D

A - you can import TF models to BQ

Model : AI Platform. pred batch data : BigQuery constraint : computational overhead Same platform as data == less computation required to load and pass it to model

minimize computational overhead–>BigQuery

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

I think D have extra compute on extrating data frm BQ

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.

Went with D

why not C?

what about C?

D is more straightforward