Make predictions for your data - Amazon SageMaker

Make predictions for your data

Use the custom model that you've built in SageMaker Canvas to make predictions for your data. The following sections show you how to make predictions for numeric and categorical prediction models, image prediction models, and text prediction models. For information about how to make predictions with a time series forecast model, see Make a time series forecast.

Numeric and categorical prediction, image prediction, and text prediction custom models support making the following types of predictions for your data:

  • Single predictions — A Single prediction is when you only need to make one prediction. For example, you have one image or passage of text that you want to classify.

  • Batch predictions — A Batch prediction is when you’d like to make predictions for an entire dataset. For example, you have a CSV file of customer reviews for which you’d like to predict the customer sentiment, or you have a folder of image files that you'd like to classify. You should make predictions with a dataset that matches your input dataset. Canvas provides you with the ability to do manual batch predictions, or you can configure automatic batch predictions that initiate whenever a specified dataset is updated in Canvas.

For each prediction or set of predictions, SageMaker Canvas returns the following:

  • The predicted values

  • The probability of the predicted value being correct

Get started

Choose one of the following workflows to make predictions with your custom model:

After generating predictions with your model, you can also do the following:

  • Update your model by creating a new version. If you want to try to improve the prediction accuracy of your model, you can build new versions of your model. You can update your data or change any advanced transformations you used, and then you can review and compare the versions of your model to choose the best one.

  • Register a model version in the SageMaker model registry. You can register versions of your model to the SageMaker model registry, which is a feature for tracking and managing the status of model versions and machine learning pipelines. A data scientist or MLOps team user with access to the SageMaker model registry can review your model versions and approve or reject them before deploying them to production.

  • Send your batch predictions to Amazon QuickSight. In Amazon QuickSight, you can build and publish dashboards with your batch prediction datasets. This can help you analyze and share results generated by your custom model.