As more and more customers are looking to put machine learning (ML) workloads in production, there is a large push in organizations to shorten the development lifecycle of ML code. Many organizations prefer writing their ML code in a production-ready style in the form of Python methods and classes as opposed to an exploratory style (writing code without using methods or classes) because this helps them ship production-ready code faster.
With Amazon SageMaker, you can use the @remote decorator to run a SageMaker training job simply by annotating your Python code with an @remote decorator. The SageMaker Python SDK will automatically translate your existing workspace environment and any associated data processing code and datasets into a SageMaker training job that runs on the SageMaker training platform.
Running a Python function locally often requires several dependencies, which may not come with the local Python runtime environment. You can install them via

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