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?
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?
To deploy a BigQuery ML-trained AutoML regression model for online prediction using Vertex AI, you should follow a systematic approach that avoids unnecessary retraining. Exporting the model from BigQuery ML to Cloud Storage first allows you to easily import it into the Vertex AI Model Registry. Once in the registry, it can be straightforwardly deployed to a Vertex AI endpoint for online prediction. This process leverages existing tools and ensures efficient deployment without requiring retraining or altering the model unnecessarily.
I think it's D, as model retraining should not be required unless it's specified there's new data.
I agree with pikachu007
I think it's C Exported models for model types AUTOML_REGRESSOR and AUTOML_CLASSIFIER do not support AI Platform deployment for online prediction.
Friend is the C, and with Alter MODEL you can register the model in Vertex AI, I work in a company and I myself have registered models like this.
Agree with Pikachu007, the option D is good.
the answer is C, 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
https://cloud.google.com/vertex-ai/docs/model-registry/model-registry-bqml https://cloud.google.com/bigquery/docs/update_vertex
https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-alter-model#alter_model_statement
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 doesn't mean retrain...
C) https://cloud.google.com/bigquery/docs/create_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
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
https://cloud.google.com/vertex-ai/docs/model-registry/model-registry-bqml
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.
why not C? it is not necessary to export in GCS
I changed my answer to C. GCS is not necessary
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