Page Reader AI | Exam Professional Machine Learning Engineer topic 1 question 175 discussion

Exam Professional Machine Learning Engineer topic 1 question 175 discussion - ExamTopics

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Question:

The question asks about the best approach to deploy a pre-trained scikit-learn model on Google Vertex AI for both online and batch prediction, while minimizing extra code. The model requires input data preprocessing.

Options:

A: Upload the model using a prebuilt scikit-learn prediction container to the Vertex AI Model Registry. Deploy to Vertex AI Endpoints, and use instanceConfig.instanceType for data transformation in a batch prediction job.
B: Wrap the model in a custom prediction routine (CPR), build a container image, upload to Vertex AI Model Registry, and deploy to Endpoints for a batch prediction job.
C: Create a custom container and serving function for the model, upload to the Model Registry, and deploy to Endpoints for a batch prediction job.
D: Create a custom container, upload to the Model Registry, deploy to Endpoints, and use instanceConfig.instanceType for data transformation in a batch prediction job.

Correct Answer:

The suggested answer is B. Wrapping the model in a custom prediction routine and building a container image is recommended for optimal deployment and efficiency.

You have recently trained a scikit-learn model that you plan to deploy on Vertex AI. This model will support both online and batch prediction. You need to preprocess input data for model inference. You want to package the model for deployment while minimizing additional code. What should you do?

A. 1. Upload your model to the Vertex AI Model Registry by using a prebuilt scikit-ieam prediction container. 2. Deploy your model to Vertex AI Endpoints, and create a Vertex AI batch prediction job that uses the instanceConfig.instanceType setting to transform your input data.
B. 1. Wrap your model in a custom prediction routine (CPR). and build a container image from the CPR local model. 2. Upload your scikit learn model container to Vertex AI Model Registry. 3. Deploy your model to Vertex AI Endpoints, and create a Vertex AI batch prediction job
C. 1. Create a custom container for your scikit learn model. 2. Define a custom serving function for your model. 3. Upload your model and custom container to Vertex AI Model Registry. 4. Deploy your model to Vertex AI Endpoints, and create a Vertex AI batch prediction job.
D. 1. Create a custom container for your scikit learn model. 2. Upload your model and custom container to Vertex AI Model Registry. 3. Deploy your model to Vertex AI Endpoints, and create a Vertex AI batch prediction job that uses the instanceConfig.instanceType setting to transform your input data.

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