Amazon SageMaker Autopilot helps you complete an end-to-end machine learning (ML) workflow by automating the steps of feature engineering, training, tuning, and deploying an ML model for inference. You provide SageMaker Autopilot with a tabular data set and a target attribute to predict. Then, SageMaker Autopilot automatically explores your data, trains, tunes, ranks and finds the best model. Finally, you can deploy this model to production for inference with one click.
What’s new?
The newly launched feature, SageMaker Autopilot Model Quality Reports, now reports your model’s metrics to provide better visibility into your model’s performance for regression and classification problems. You can leverage these metrics to gather more insights about the best model in the Model leaderboard.
These metrics and reports that are available in a new “Performance” tab under the “Model details” of the best model include confusion matrices, an area under the receiver operating characteristic (AUC-ROC) curve and

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