You need to build classification workflows over several structured datasets currently stored in BigQuery. Because you will be performing the classification several times, you want to complete the following steps without writing code: exploratory data analysis, feature selection, model building, training, and hyperparameter tuning and serving. What should you do?
To build classification workflows over structured datasets stored in BigQuery without writing code for tasks like exploratory data analysis, feature selection, model building, training, hyperparameter tuning, and serving, AutoML Tables is the best option. AutoML Tables provides a user-friendly interface with automated machine learning capabilities that handle all these steps without requiring the user to write code.
A. Because BigQuery ML need to write code.
=New Question7= You recently designed and built a custom neural network that uses critical dependencies specific to your organization's framework. You need to train the model using a managed training service on Google Cloud. However, the ML framework and related dependencies are not supported by Al Platform Training. Also, both your model and your data are too large to fit in memory on a single machine. Your ML framework of choice uses the scheduler, workers, and servers distribution structure. What should you do? A. Build your custom container to run jobs on Al Platform Training B. Use a built-in model available on Al Platform Training C. Build your custom containers to run distributed training jobs on Al Platform Training D. Reconfigure your code to a ML framework with dependencies that are supported by Al Platform Training
Answer?
C custom container and distributed system
Answer - C It's between A & C C - Because the questions states data too large to fit in memory hence distributed training is relevant
C is the answer without doubt. A: Distributed? Nope B: Built-in? Nope D: Reconfig? Nope
A is correct https://cloud.google.com/automl-tables/docs/beginners-guide
A is correct
The question says 'over several structured datasets' means large/multiple datasets and 'several times' means frequently use of data. Though BigQuery ML is not an absolute 'NO Code' solution but all it needs is very simple SQL query to train ML model So 'B' could be the correct answer here but it is asking for Hyperparameter tuning which is not available in BigQuery ML so correct answer is 'A'
You have to export out BQ trained ML model to set it up for inference. Inference is not natively offered in BQ. You can perform EDA in autoML tables.
"without writing code" only A option complies with this statment , all other options requires writing code
Because BigQuery ML need to write code, so A is the correct one
Correct answer is "A"
Correct answer is "A"
"without writing code" ==> AutoML A is correct
Because BigQueryML doesn't have lots of steps that mentioned in question
Went with A
requirement : No code A. Configure AutoML Tables to perform the classification task. : No code B. Run a BigQuery ML task to perform logistic regression for the classification. : coding LR model C. Use AI Platform Notebooks to run the classification model with pandas library. : Notebooks include codes D. Use AI Platform to run the classification model job configured for hyperparameter tuning.: job needs to be written what to execute
A - AutoML is no code
A) Auto ML Tables doesn’t require code.
create a model without doing literally anything, logo AutoML. A.