AutoMLChannel - Amazon SageMaker
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A channel is a named input source that training algorithms can consume. The validation dataset size is limited to less than 2 GB. The training dataset size must be less than 100 GB. For more information, see Channel.


A validation dataset must contain the same headers as the training dataset.



The name of the target variable in supervised learning, usually represented by 'y'.

Type: String

Length Constraints: Minimum length of 1.

Required: Yes


The channel type (optional) is an enum string. The default value is training. Channels for training and validation must share the same ContentType and TargetAttributeName. For information on specifying training and validation channel types, see How to specify training and validation datasets.

Type: String

Valid Values: training | validation

Required: No


You can use Gzip or None. The default value is None.

Type: String

Valid Values: None | Gzip

Required: No


The content type of the data from the input source. You can use text/csv;header=present or x-application/ The default value is text/csv;header=present.

Type: String

Length Constraints: Maximum length of 256.

Pattern: .*

Required: No


The data source for an AutoML channel.

Type: AutoMLDataSource object

Required: No


If specified, this column name indicates which column of the dataset should be treated as sample weights for use by the objective metric during the training, evaluation, and the selection of the best model. This column is not considered as a predictive feature. For more information on Autopilot metrics, see Metrics and validation.

Sample weights should be numeric, non-negative, with larger values indicating which rows are more important than others. Data points that have invalid or no weight value are excluded.

Support for sample weights is available in Ensembling mode only.

Type: String

Length Constraints: Minimum length of 1. Maximum length of 256.

Pattern: ^[a-zA-Z0-9_-]+$

Required: No

See Also

For more information about using this API in one of the language-specific Amazon SDKs, see the following: