@@ -52,7 +52,7 @@ def __init__(self, role, train_instance_count, train_instance_type, k, init_meth
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:class:`~sagemaker.amazon.record_pb2.Record` protobuf serialized data to be stored in S3.
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To learn more about the Amazon protobuf Record class and how to prepare bulk data in this format, please
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- consult AWS technical documentation: https://alpha- docs- aws.amazon.com/sagemaker/latest/dg/cdf-training.html
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+ consult AWS technical documentation: https://docs. aws.amazon.com/sagemaker/latest/dg/cdf-training.html.
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After this Estimator is fit, model data is stored in S3. The model may be deployed to an Amazon SageMaker
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Endpoint by invoking :meth:`~sagemaker.amazon.estimator.EstimatorBase.deploy`. As well as deploying an Endpoint,
@@ -61,14 +61,13 @@ def __init__(self, role, train_instance_count, train_instance_type, k, init_meth
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KMeans Estimators can be configured by setting hyperparameters. The available hyperparameters for KMeans
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are documented below. For further information on the AWS KMeans algorithm, please consult AWS technical
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- documentation: https://alpha- docs- aws.amazon.com/sagemaker/latest/dg/k-means.html
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+ documentation: https://docs. aws.amazon.com/sagemaker/latest/dg/k-means.html.
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Args:
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role (str): An AWS IAM role (either name or full ARN). The Amazon SageMaker training jobs and
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APIs that create Amazon SageMaker endpoints use this role to access
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training data and model artifacts. After the endpoint is created,
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the inference code might use the IAM role, if accessing AWS resource.
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- For more information, see <link>???.
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train_instance_count (int): Number of Amazon EC2 instances to use for training.
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train_instance_type (str): Type of EC2 instance to use for training, for example, 'ml.c4.xlarge'.
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k (int): The number of clusters to produce.
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