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Library . Amazon . MachineLearning
Amazon Machine Learning makes it easy for developers to build smart applications that process new data and generate predictions for your application.
To use the Choreos in this bundle, you'll need an Amazon AWS Secret Key and an Amazon AWS Access Key, which are used to authenticate your account. If you already have an Amazon AWS account, you can find both keys by logging into the AWS Console and going to the Security Credentials area. If you don't have an Amazon account, you can sign up for one here.
To generate a Prediction, you'll need to create an MLModel. To get started, we recommend going through this Amazon Machine Learning Tutorial to create your first MLModel via the AWS Console.
The following Machine Learning Choreos require that you grant Amazon ML the appropriate permissions to access data in an S3 bucket:
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Adds one or more tags to an object, up to a limit of 10.
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Generates predictions for a group of observations.
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Creates a DataSource object.
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Creates a new Evaluation of an MLModel.
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Creates a new MLModel using the DataSource and the recipe as information sources.
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This Choreo creates a real-time endpoint for the MLModel. The endpoint contains the URI of the MLModel which is the location to send real-time prediction requests for the specified MLModel.
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Assigns the DELETED status to a BatchPrediction, rendering it unusable.
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Assigns the DELETED status to a DataSource, rendering it unusable.
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Assigns the DELETED status to an Evaluation, rendering it unusable.
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Assigns the DELETED status to an MLModel, rendering it unusable.
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Deletes a real time endpoint of an MLModel.
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Deletes the specified tags associated with an ML object.
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Returns a list of DataSources that match the search criteria in the request.
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Returns a list of Evaluations that match the search criteria in the request.
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Returns a list of MLModels that match the search criteria in the request.
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Describes one or more of the tags for your Amazon ML object.
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Returns a DataSource that includes metadata and data file information, as well as the current status of the DataSource.
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Returns an Evaluation that includes metadata as well as the current status of the Evaluation.
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Returns an MLModel that includes detailed metadata, data source information, and the current status of the MLModel.
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Generates a prediction for the observation using the specified ML Model.
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Updates the BatchPredictionName of a BatchPrediction.
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Updates the DataSourceName of a DataSource.
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Updates the EvaluationName of an Evaluation.
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Updates the MLModelName and the ScoreThreshold of an MLModel.
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