A company wants to migrate ML models from an on-premises environment to Amazon SageMaker AI. The models are based on the PyTorch algorithm. The company needs to reuse its existing custom scripts as…

The correct answer is D. SageMaker AI script mode. SageMaker Script Mode allows ML engineers to bring their own custom Python training scripts and run them within SageMaker's managed training infrastructure (choosing a framework container such as PyTorch). The existing scripts require minimal or no modification - you supply the…

ML Model Development

Question

A company wants to migrate ML models from an on-premises environment to Amazon SageMaker AI. The models are based on the PyTorch algorithm. The company needs to reuse its existing custom scripts as much as possible on AWS. Which feature of SageMaker AI should the company use to meet these requirements?

Options

ASageMaker AI built-in algorithms
BSageMaker Canvas
CSageMaker JumpStart
DSageMaker AI script mode

How the community answered

(67 responses)

A
4% (3)
B
12% (8)
C
3% (2)
D
81% (54)

Explanation

SageMaker Script Mode allows ML engineers to bring their own custom Python training scripts and run them within SageMaker's managed training infrastructure (choosing a framework container such as PyTorch). The existing scripts require minimal or no modification - you supply the script, SageMaker handles the compute, networking, and storage. This maximizes reuse of existing code. Built-in algorithms (A) are pre-built and don't accept custom scripts. SageMaker Canvas (B) is a no-code AutoML UI with no scripting capability. SageMaker JumpStart (C) provides pre-trained foundation models and solutions but is not designed for running custom training scripts.

Topics

#SageMaker Script Mode#PyTorch#Model Migration#Custom Training

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