aws lookoutequipment create-model
Creates an ML model for data inference. A machine-learning (ML) model is a mathematical model that finds patterns in your data. In Amazon Lookout for Equipment, the model learns the patterns of normal behavior and detects abnormal behavior that could be potential equipment failure (or maintenance events). The models are made by analyzing normal data and abnormalities in machine behavior that have already occurred. Your model is trained using a portion of the data from your dataset and uses that data to learn patterns of normal behavior and abnormal patterns that lead to equipment failure. Another portion of the data is used to evaluate the model's accuracy
Options
| Name | Description |
|---|---|
--model-name <string> | The name for the ML model to be created |
--dataset-name <string> | The name of the dataset for the ML model being created |
--dataset-schema <structure> | The data schema for the ML model being created |
--labels-input-configuration <structure> | The input configuration for the labels being used for the ML model that's being created |
--client-token <string> | A unique identifier for the request. If you do not set the client request token, Amazon Lookout for Equipment generates one |
--training-data-start-time <timestamp> | Indicates the time reference in the dataset that should be used to begin the subset of training data for the ML model |
--training-data-end-time <timestamp> | Indicates the time reference in the dataset that should be used to end the subset of training data for the ML model |
--evaluation-data-start-time <timestamp> | Indicates the time reference in the dataset that should be used to begin the subset of evaluation data for the ML model |
--evaluation-data-end-time <timestamp> | Indicates the time reference in the dataset that should be used to end the subset of evaluation data for the ML model |
--role-arn <string> | The Amazon Resource Name (ARN) of a role with permission to access the data source being used to create the ML model |
--data-pre-processing-configuration <structure> | The configuration is the TargetSamplingRate, which is the sampling rate of the data after post processing by Amazon Lookout for Equipment. For example, if you provide data that has been collected at a 1 second level and you want the system to resample the data at a 1 minute rate before training, the TargetSamplingRate is 1 minute. When providing a value for the TargetSamplingRate, you must attach the prefix "PT" to the rate you want. The value for a 1 second rate is therefore PT1S, the value for a 15 minute rate is PT15M, and the value for a 1 hour rate is PT1H |
--server-side-kms-key-id <string> | Provides the identifier of the AWS KMS customer master key (CMK) used to encrypt model data by Amazon Lookout for Equipment |
--tags <list> | Any tags associated with the ML model being created |
--cli-input-json <string> | Performs service operation based on the JSON string provided. The JSON string follows the format provided by ``--generate-cli-skeleton``. If other arguments are provided on the command line, the CLI values will override the JSON-provided values. It is not possible to pass arbitrary binary values using a JSON-provided value as the string will be taken literally |
--generate-cli-skeleton <string> | Prints a JSON skeleton to standard output without sending an API request. If provided with no value or the value ``input``, prints a sample input JSON that can be used as an argument for ``--cli-input-json``. If provided with the value ``output``, it validates the command inputs and returns a sample output JSON for that command |