Make single predictions
Note
This section describes how to get single predictions from your model inside the Canvas application. For information about making real-time invocations in a production environment by deploying your model to an endpoint, see Deploy your models to an endpoint.
Make single predictions if you want to get a prediction for a single data point. You can use this feature to get real-time predictions or to experiment with changing individual values to see how they impact the prediction outcome. Note that single predictions rely on an Asynchronous Inference endpoint, which shuts down after being idle (or not receiving any prediction requests) for two hours.
Choose one of the following procedures based on your model type.
Make single predictions with numeric and categorical prediction models
To make a single prediction for a numeric or categorical prediction model, do the following:
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In the left navigation pane of the Canvas application, choose My models.
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On the My models page, choose your model.
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After opening your model, choose the Predict tab.
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On the Run predictions page, choose Single prediction.
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For each Column field, which represents the columns of your input data, you can change the Value. Select the dropdown menu for the Value you want to change. For numeric fields, you can enter a new number. For fields with labels, you can select a different label.
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When you’re ready to generate the prediction, in the right Prediction pane, choose Update.
In the right Prediction pane, you’ll see the prediction result. You can Copy the prediction result chart, or you can also choose Download to either download the prediction result chart as an image or to download the values and prediction as a CSV file.
Make single predictions with time series forecasting models
To make a single prediction for a time series forecasting model, do the following:
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In the left navigation pane of the Canvas application, choose My models.
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On the My models page, choose your model.
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After opening your model, choose the Predict tab.
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Choose Single prediction.
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For Item, select the item for which you want to forecast values.
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If you used a group by column to train the model, then select the group by category for the item.
The prediction result loads in the pane below, showing you a chart with the forecast for each quantile. Choose Schema view to see the numeric predicted values. You can also choose Download to download the prediction results as either an image or a CSV file.
Make single predictions with image prediction models
To make a single prediction for a single-label image prediction model, do the following:
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In the left navigation pane of the Canvas application, choose My models.
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On the My models page, choose your model.
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After opening your model, choose the Predict tab.
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On the Run predictions page, choose Single prediction.
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Choose Import image.
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You’ll be prompted to upload an image. You can upload an image from your local computer or from an Amazon S3 bucket.
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Choose Import to import your image and generate the prediction.
In the right Prediction results pane, the model lists the possible labels for the image along with a Confidence score for each label. For example, the model might predict the label Sea for an image, with a confidence score of 96%. The model may have predicted the image as a Glacier with only a confidence score of 4%. Therefore, you can determine that your model is fairly confident in predicting images of the sea.
Make single predictions with text prediction models
To make a single prediction for a multi-category text prediction model, do the following:
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In the left navigation pane of the Canvas application, choose My models.
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On the My models page, choose your model.
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After opening your model, choose the Predict tab.
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On the Run predictions page, choose Single prediction.
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For the Text field, enter the text for which you’d like to get a prediction.
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Choose Generate prediction results to get your prediction.
In the right Prediction results pane, you receive an analysis of your text in addition to a Confidence score for each possible label. For example, if you entered a good review for a product, you might get Positive with a confidence score of 85%, while the confidence score for Neutral might be 10% and the confidence score for Negative only 5%.