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Move Amazon SageMaker Autopilot ML models from experimentation to production using Amazon SageMaker Pipelines

AWS Machine Learning

It is a sampled version of the “ Diabetes 130-US hospitals for years 1999-2008 Data Set”. When the registered model meets the expected performance requirements after a manual review, you can deploy the model to a SageMaker endpoint using a standalone deployment script. script creates an Autopilot job. SageMaker pipeline steps.

Scripts 77
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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. The sample dataset we use in this post is a sampled version of the Diabetes 130-US hospitals for years 1999-2008 Data Set (Beata Strack, Jonathan P.