You recently used BigQuery ML to train an AutoML regression model. You shared results with your team and received positive feedback. You need to deploy your model for online prediction as quickly as possible. What should you do?
A.
Retrain the model by using BigQuery ML, and specify Vertex AI as the model registry. Deploy the model from Vertex AI Model Registry to a Vertex AI endpoint,
B.
Retrain the model by using Vertex Al Deploy the model from Vertex AI Model. Registry to a Vertex AI endpoint.
C.
Alter the model by using BigQuery ML, and specify Vertex AI as the model registry. Deploy the model from Vertex AI Model Registry to a Vertex AI endpoint.
D.
Export the model from BigQuery ML to Cloud Storage. Import the model into Vertex AI Model Registry. Deploy the model to a Vertex AI endpoint.
You can use the ALTER MODEL statement to register your existing BigQuery ML model with Vertex AI Model Registry
https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-alter-model
C is the best option because:
1) Retraining the model (A/B) is not necessary (see positive feedbacks)
2) Exporting to Cloud Storage (D) is not necessary, since you can use the ALTER MODEL statement to register it on Vertex AI Model Registry and deploy it to the Vertex AI endpoint from there
3) Using BigQuery ML without exporting the model is the quickiest option
D is the best option because:
1) BigQuery ML is excellent for training, not much for online prediction
2) Vertex AI provides a more robust and scalable infrastructure.
3) Exporting model from BigQuery ML to a format compatible with Vertex AI (typically Cloud Storage) is required
***A, B, and C attempt to directly deploy from BigQuery ML, which isn't a supported workflow.
C is correct Here's why:
1) You trained an AutoML regression model using BigQuery ML.
2)To deploy the model for online prediction, you need to export the model in a format that is compatible with Vertex AI.
3)Altering the model by using BigQuery ML and specifying Vertex AI as the model registry allows you to export the model in the correct format.
Once exported, you can deploy the model from Vertex AI Model Registry to a Vertex AI endpoint, which enables online prediction
C is correct Here's why:
1) You trained an AutoML regression model using BigQuery ML.
2)To deploy the model for online prediction, you need to export the model in a format that is compatible with Vertex AI.
3)Altering the model by using BigQuery ML and specifying Vertex AI as the model registry allows you to export the model in the correct format.
Once exported, you can deploy the model from Vertex AI Model Registry to a Vertex AI endpoint, which enables online prediction
You recently used BigQuery ML to train an AutoML regression model. You shared results with your team and received positive feedback. You need to deploy your model for online prediction as quickly as possible. What should you do?
A. Retrain the model by using BigQuery ML, and specify Vertex AI as the model registry. Deploy the model from Vertex AI Model Registry to a Vertex AI endpoint,
B. Retrain the model by using Vertex Al Deploy the model from Vertex AI Model. Registry to a Vertex AI endpoint.
C. Alter the model by using BigQuery ML, and specify Vertex AI as the model registry. Deploy the model from Vertex AI Model Registry to a Vertex AI endpoint.
D. Export the model from BigQuery ML to Cloud Storage. Import the model into Vertex AI Model Registry. Deploy the model to a Vertex AI endpoint.
No Retraining: You've already trained a successful model in BigQuery ML. Retraining (Options A, B, and C) is unnecessary and adds time.
Direct Deployment: Option D leverages existing tools for streamlined deployment. You export the model directly from BigQuery ML and import it into Vertex AI Model Registry for centralized management. Finally, you deploy the model to a Vertex AI endpoint for online predictions.
Cloud Storage: Cloud Storage provides a readily accessible location to store your exported model before deployment.
Alter the model is correct,no need to export the model : "You can register BigQuery ML models with the Model Registry, in order to manage them alongside your other ML models without needing to export them"
https://cloud.google.com/bigquery/docs/managing-models-vertex
a simple update is sufficient :
https://cloud.google.com/bigquery/docs/update_vertex
I think the answer here is B , because even if we alter or export automl regressor model trained in BQML is not supported in vertex ai for online prediction so we need to retrain using vertex ai
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