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Create Model

lookoutequipment_create_model R Documentation

Creates a machine learning model for data inference

Description

Creates a machine learning 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.

Usage

lookoutequipment_create_model(ModelName, DatasetName, DatasetSchema,
  LabelsInputConfiguration, ClientToken, TrainingDataStartTime,
  TrainingDataEndTime, EvaluationDataStartTime, EvaluationDataEndTime,
  RoleArn, DataPreProcessingConfiguration, ServerSideKmsKeyId, Tags,
  OffCondition, ModelDiagnosticsOutputConfiguration)

Arguments

ModelName

[required] The name for the machine learning model to be created.

DatasetName

[required] The name of the dataset for the machine learning model being created.

DatasetSchema

The data schema for the machine learning model being created.

LabelsInputConfiguration

The input configuration for the labels being used for the machine learning model that's being created.

ClientToken

[required] A unique identifier for the request. If you do not set the client request token, Amazon Lookout for Equipment generates one.

TrainingDataStartTime

Indicates the time reference in the dataset that should be used to begin the subset of training data for the machine learning model.

TrainingDataEndTime

Indicates the time reference in the dataset that should be used to end the subset of training data for the machine learning model.

EvaluationDataStartTime

Indicates the time reference in the dataset that should be used to begin the subset of evaluation data for the machine learning model.

EvaluationDataEndTime

Indicates the time reference in the dataset that should be used to end the subset of evaluation data for the machine learning model.

RoleArn

The Amazon Resource Name (ARN) of a role with permission to access the data source being used to create the machine learning model.

DataPreProcessingConfiguration

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

ServerSideKmsKeyId

Provides the identifier of the KMS key used to encrypt model data by Amazon Lookout for Equipment.

Tags

Any tags associated with the machine learning model being created.

OffCondition

Indicates that the asset associated with this sensor has been shut off. As long as this condition is met, Lookout for Equipment will not use data from this asset for training, evaluation, or inference.

ModelDiagnosticsOutputConfiguration

The Amazon S3 location where you want Amazon Lookout for Equipment to save the pointwise model diagnostics. You must also specify the RoleArn request parameter.

Value

A list with the following syntax: 

list(
  ModelArn = "string",
  Status = "IN_PROGRESS"|"SUCCESS"|"FAILED"|"IMPORT_IN_PROGRESS"
)

Request syntax

svc$create_model(
  ModelName = "string",
  DatasetName = "string",
  DatasetSchema = list(
    InlineDataSchema = "string"
  ),
  LabelsInputConfiguration = list(
    S3InputConfiguration = list(
      Bucket = "string",
      Prefix = "string"
    ),
    LabelGroupName = "string"
  ),
  ClientToken = "string",
  TrainingDataStartTime = as.POSIXct(
    "2015-01-01"
  ),
  TrainingDataEndTime = as.POSIXct(
    "2015-01-01"
  ),
  EvaluationDataStartTime = as.POSIXct(
    "2015-01-01"
  ),
  EvaluationDataEndTime = as.POSIXct(
    "2015-01-01"
  ),
  RoleArn = "string",
  DataPreProcessingConfiguration = list(
    TargetSamplingRate = "PT1S"|"PT5S"|"PT10S"|"PT15S"|"PT30S"|"PT1M"|"PT5M"|"PT10M"|"PT15M"|"PT30M"|"PT1H"
  ),
  ServerSideKmsKeyId = "string",
  Tags = list(
    list(
      Key = "string",
      Value = "string"
    )
  ),
  OffCondition = "string",
  ModelDiagnosticsOutputConfiguration = list(
    S3OutputConfiguration = list(
      Bucket = "string",
      Prefix = "string"
    ),
    KmsKeyId = "string"
  )
)