Amazon SageMaker Autopilot is an automated machine learning (AutoML) solution that performs all the tasks you need to complete an end-to-end machine learning (ML) workflow. It explores and prepares your data, applies different algorithms to generate a model, and transparently provides model insights and explainability reports to help you interpret the results. Autopilot can also create a real-time endpoint for online inference. You can access Autopilot’s one-click features in Amazon SageMaker Studio or by using the AWS SDK for Python (Boto3) or the SageMaker Python SDK.
In this post, we show how to make batch predictions on an unlabeled dataset using an Autopilot-trained model. We use a synthetically generated dataset that is indicative of the types of features you typically see when predicting customer churn.
Solution overview
Batch inference, or offline inference, is the process of generating predictions on a batch of observations. Batch inference assumes you don’t need an

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