{"id":1950,"date":"2022-03-10T18:45:52","date_gmt":"2022-03-10T18:45:52","guid":{"rendered":"https:\/\/salarydistribution.com\/machine-learning\/2022\/03\/10\/make-batch-predictions-with-amazon-sagemaker-autopilot\/"},"modified":"2022-03-10T18:45:52","modified_gmt":"2022-03-10T18:45:52","slug":"make-batch-predictions-with-amazon-sagemaker-autopilot","status":"publish","type":"post","link":"https:\/\/salarydistribution.com\/machine-learning\/2022\/03\/10\/make-batch-predictions-with-amazon-sagemaker-autopilot\/","title":{"rendered":"Make batch predictions with Amazon SageMaker Autopilot"},"content":{"rendered":"
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Amazon SageMaker Autopilot<\/a> 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\u2019s one-click features in Amazon SageMaker Studio<\/a> or by using the AWS SDK for Python (Boto3)<\/a> or the SageMaker Python SDK.<\/p>\n

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.<\/p>\n

Solution overview<\/h2>\n

Batch <\/em>inference, or offline <\/em>inference, is the process of generating predictions on a batch of observations. Batch inference assumes you don\u2019t need an immediate response to a model prediction request, as you would when using an online, real-time model endpoint. Offline predictions are suitable for larger datasets and in cases where you can afford to wait several minutes or hours for a response. In contrast, online <\/em>inference generates ML predictions in real time, and is aptly referred to as real-time<\/em> inference or dynamic<\/em> inference. Typically, these predictions are generated on a single observation of data at runtime.<\/p>\n

Losing customers is costly for any business. Identifying unhappy customers early on gives you a chance to offer them incentives to stay. Mobile operators have historical customer data showing those who have churned and those who have maintained service. We can use this historical information to construct a model to predict if a customer will churn using ML.<\/p>\n

After we train an ML model, we can pass the profile information of an arbitrary customer (the same profile information that we used for training) to the model, and have the model predict whether or not the customer will churn. The dataset used for this post is hosted under the sagemaker-sample-files folder in an Amazon Simple Storage Service<\/a> (Amazon S3) public bucket, which you can download. It consists of 5,000 records, where each record uses 21 attributes to describe the profile of a customer for an unknown US mobile operator. The attributes are as follows:<\/p>\n