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Automated exploratory data analysis and model operationalization framework with a human in the loop

AWS Machine Learning

According to a Forbes survey , there is widespread consensus among ML practitioners that data preparation accounts for approximately 80% of the time spent in developing a viable ML model. For instructions on assigning permissions to the role, refer to Amazon SageMaker API Permissions: Actions, Permissions, and Resources Reference.