Access private repos using the @remote decorator for Amazon SageMaker training workloads
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 …
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