Amazon SageMaker Autopilot automatically builds, trains, and tunes the best custom machine learning (ML) models based on your data. It’s an automated machine learning (AutoML) solution that eliminates the heavy lifting of handwritten ML models that requires ML expertise. Data scientists need to only provide a tabular dataset and select the target column to predict, and Autopilot automatically infers the problem type, performs data preprocessing and feature engineering, selects the algorithms and training mode, and explores different configurations to find the best ML model. Then you can directly deploy the model to an Amazon SageMaker endpoint or iterate on the recommended solutions to further improve the model quality.
Although Autopilot eliminates the heavy lifting of building ML models, MLOps engineers still have to create, automate, and manage end-to-end ML workflows. Amazon SageMaker Pipelines helps you automate the different steps of the ML lifecycle, including data preprocessing, training, tuning and evaluating

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