MLA-C01 · Question #151
A company has several teams that have developed separate prediction models on their own laptops. The teams developed the models by using Python with scikit-learn and TensorFlow frameworks. The…
The correct answer is D. Import the Python-based models into SageMaker. Rebuild the scikit-learn and TensorFlow. The least operational overhead comes from directly importing the scikit-learn and TensorFlow models into SageMaker, rebuilding them using the respective prebuilt SageMaker frameworks, and then registering them in the SageMaker Model Registry. This leverages managed framework…
Question
A company has several teams that have developed separate prediction models on their own laptops. The teams developed the models by using Python with scikit-learn and TensorFlow frameworks. The company must rebuild the models and must integrate the models into an ML infrastructure that the company manages by using Amazon SageMaker. The company also must incorporate the models into a model registry. Which solution will meet these requirements with the LEAST operational overhead?
Options
- AExport the models from the laptops to an Amazon S3 bucket. Use an Amazon API Gateway
- BImport the models into the SageMaker Model Registry. Use SageMaker to run the imported
- CUse code from the laptops to create containers for the models. Use the bring your own container
- DImport the Python-based models into SageMaker. Rebuild the scikit-learn and TensorFlow
How the community answered
(32 responses)- A19% (6)
- B9% (3)
- C3% (1)
- D69% (22)
Explanation
The least operational overhead comes from directly importing the scikit-learn and TensorFlow models into SageMaker, rebuilding them using the respective prebuilt SageMaker frameworks, and then registering them in the SageMaker Model Registry. This leverages managed framework containers provided by SageMaker, avoids custom container management, and integrates seamlessly with the registry.
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