![]() ![]() This is often referred to as training and tuning the model. After it choose an algorithm, Autopilot optimizes its performance using a hyperparameter optimization search process. Because the target attribute is binary, our model performs binary prediction, also known as binary classification.ĭownload the dataset to your local development environment and explore it by running the following S3 copy command with the AWS Command Line Interface (AWS CLI):Īfter Autopilot begins an experiment, the service automatically inspects the raw input data, applies feature processors, and picks the best set of algorithms. The last attribute, Churn?, is the target attribute that we want the ML model to predict. Churn? – Customer left the service: True/False.CustServ Calls – Number of calls placed to Customer Service.Intl Mins, Intl Calls, Intl Charge – Billed cost for international calls.Night Mins, Night Calls, Night Charge – Billed cost for calls placed during nighttime.Eve Mins, Eve Calls, Eve Charge – Billed cost for calls placed during the evening.Day Charge – Billed cost of daytime calls.Day Calls – Total number of calls placed during the day.Day Mins – Total number of calling minutes used during the day.VMail Message – Average number of voice mail messages per month.VMail Plan – Has a voice mail feature: Yes/No.Int’l Plan – Has an international calling plan: Yes/No.Phone – Remaining seven-digit phone number.Area Code – Three-digit area code of the corresponding customer’s phone number.Account Length – Number of days that this account has been active.State – US state in which the customer resides, indicated by a two-letter abbreviation for example, TX or CA.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 dataset used for this post is hosted under the sagemaker-sample-files folder in an Amazon Simple Storage Service (Amazon S3) public bucket, which you can download. We can use this historical information to construct a model to predict if a customer will churn using ML.Īfter 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. Mobile operators have historical customer data showing those who have churned and those who have maintained service. Identifying unhappy customers early on gives you a chance to offer them incentives to stay. Losing customers is costly for any business. Typically, these predictions are generated on a single observation of data at runtime. ![]() ![]() In contrast, online inference generates ML predictions in real time, and is aptly referred to as real-time inference or dynamic inference. Offline predictions are suitable for larger datasets and in cases where you can afford to wait several minutes or hours for a response. Batch inference assumes you don’t need an immediate response to a model prediction request, as you would when using an online, real-time model endpoint. Solution overviewīatch inference, or offline inference, is the process of generating predictions on a batch of observations. We use a synthetically generated dataset that is indicative of the types of features you typically see when predicting customer churn. In this post, we show how to make batch predictions on an unlabeled dataset using an Autopilot-trained model. 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. Autopilot can also create a real-time endpoint for online inference. 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. 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. ![]()
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