HOTSPOT - A company wants to host an ML model on Amazon SageMaker. An ML engineer is configuring a continuous integration and continuous delivery (Cl/CD) pipeline in AWS CodePipeline to deploy the model. The pipeline must run automatically when new training data for the model is uploaded to an Amazon S3 bucket. Select and order the pipeline's correct steps from the following list. Each step should be selected one time or not at all. (Select and order three.) • An S3 event notification invokes the pipeline when new data is uploaded. • S3 Lifecycle rule invokes the pipeline when new data is uploaded. • SageMaker retrains the model by using the data in the S3 bucket. • The pipeline deploys the model to a SageMaker endpoint. • The pipeline deploys the model to SageMaker Model Registry.
Q7Deployment and Orchestration of ML Workflows
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Discussion · 4
9
1. An S3 event notification starts the pipeline when new data is uploaded.
2. SageMaker retrains the model using the data in the S3 bucket.
3. The pipeline deploys the model to a SageMaker endpoint.
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The first two steps are obvious. For the last (third) step, there are two options.
1. The pipeline deploys the model to a SageMaker endpoint.
2. The pipeline deploys the model to SageMaker Model Registry.
Since the question says deploy the model, option 1 is correct. If we add the model to Model Registry, it will just sit in the catalog, but it won't get deployed. It needs to be explicitly deployed to the endpoint. So 2 is the correct third step.
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I suppose you had a typo in "So 2 is the correct third step.".
The model should be deployed to an endpoint (not registry):
• An S3 event notification starts the pipeline when new data is uploaded.
• SageMaker retrains the model by using the data in the S3 bucket.
• The pipeline deploys the model to a SageMaker endpoint.
1
An S3 notification is invoked when a new data is uploaded
Sagemaker then trains the model with the new data
The pipeline deploys the newly trained model to a sagemaker endpoint