Topic 1 Question 222
2 つ選択A data scientist at a financial services company used Amazon SageMaker to train and deploy a model that predicts loan defaults. The model analyzes new loan applications and predicts the risk of loan default. To train the model, the data scientist manually extracted loan data from a database. The data scientist performed the model training and deployment steps in a Jupyter notebook that is hosted on SageMaker Studio notebooks. The model's prediction accuracy is decreasing over time.
Which combination of steps is the MOST operationally efficient way for the data scientist to maintain the model's accuracy?
Use SageMaker Pipelines to create an automated workflow that extracts fresh data, trains the model, and deploys a new version of the model.
Configure SageMaker Model Monitor with an accuracy threshold to check for model drift. Initiate an Amazon CloudWatch alarm when the threshold is exceeded. Connect the workflow in SageMaker Pipelines with the CloudWatch alarm to automatically initiate retraining.
Store the model predictions in Amazon S3. Create a daily SageMaker Processing job that reads the predictions from Amazon S3, checks for changes in model prediction accuracy, and sends an email notification if a significant change is detected.
Rerun the steps in the Jupyter notebook that is hosted on SageMaker Studio notebooks to retrain the model and redeploy a new version of the model.
Export the training and deployment code from the SageMaker Studio notebooks into a Python script. Package the script into an Amazon Elastic Container Service (Amazon ECS) task that an AWS Lambda function can initiate.
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コメント(6)
- 正解だと思う選択肢: AB👍 2Jerry842023/02/14
- 正解だと思う選択肢: AB
Using SageMaker Pipelines to create an automated workflow that extracts fresh data, trains the model, and deploys a new version of the model is an efficient way to automate the process of model retraining and deployment.
Configuring SageMaker Model Monitor with an accuracy threshold to check for model drift and initiating an Amazon CloudWatch alarm when the threshold is exceeded is an efficient way to monitor the accuracy of the deployed model and initiate retraining when necessary. This approach helps to maintain the accuracy of the model over time.
👍 2AjoseO2023/02/20 - 正解だと思う選択肢: AB
The MOST operationally efficient way for the data scientist to maintain the model's accuracy would be to choose options A and B:
A. Use SageMaker Pipelines to create an automated workflow that extracts fresh data, trains the model, and deploys a new version of the model.
Using SageMaker Pipelines allows the data scientist to automate the entire workflow from data extraction to model deployment. This ensures that the model is trained and deployed on the latest data automatically without the need for manual intervention. The data scientist can set up the pipeline to run on a schedule or trigger it based on certain events.
B. Configure SageMaker Model Monitor with an accuracy threshold to check for model drift. Initiate an Amazon CloudWatch alarm when the threshold is exceeded. Connect the workflow in SageMaker Pipelines with the CloudWatch alarm to automatically initiate retraining.
👍 2oso03482023/03/04
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