LookoutEquipment class

Amazon Lookout for Equipment is a machine learning service that uses advanced analytics to identify anomalies in machines from sensor data for use in predictive maintenance.

Constructors

LookoutEquipment({required String region, AwsClientCredentials? credentials, AwsClientCredentialsProvider? credentialsProvider, Client? client, String? endpointUrl, bool useFipsEndpoint = false, bool useDualStackEndpoint = false})
factory

Properties

hashCode → int
The hash code for this object.
no setterinherited
runtimeType → Type
A representation of the runtime type of the object.
no setterinherited

Methods

close() → void
Closes the internal HTTP client if none was provided at creation. If a client was passed as a constructor argument, this becomes a noop.
createDataset({required String datasetName, String? clientToken, DatasetSchema? datasetSchema, String? serverSideKmsKeyId, List<Tag>? tags}) → Future<CreateDatasetResponse>
Creates a container for a collection of data being ingested for analysis. The dataset contains the metadata describing where the data is and what the data actually looks like. For example, it contains the location of the data source, the data schema, and other information. A dataset also contains any tags associated with the ingested data.
createInferenceScheduler({required InferenceInputConfiguration dataInputConfiguration, required InferenceOutputConfiguration dataOutputConfiguration, required DataUploadFrequency dataUploadFrequency, required String inferenceSchedulerName, required String modelName, required String roleArn, String? clientToken, int? dataDelayOffsetInMinutes, String? serverSideKmsKeyId, List<Tag>? tags}) → Future<CreateInferenceSchedulerResponse>
Creates a scheduled inference. Scheduling an inference is setting up a continuous real-time inference plan to analyze new measurement data. When setting up the schedule, you provide an S3 bucket location for the input data, assign it a delimiter between separate entries in the data, set an offset delay if desired, and set the frequency of inferencing. You must also provide an S3 bucket location for the output data.
createLabel({required DateTime endTime, required String labelGroupName, required LabelRating rating, required DateTime startTime, String? clientToken, String? equipment, String? faultCode, String? notes}) → Future<CreateLabelResponse>
Creates a label for an event.
createLabelGroup({required String labelGroupName, String? clientToken, List<String>? faultCodes, List<Tag>? tags}) → Future<CreateLabelGroupResponse>
Creates a group of labels.
createModel({required String datasetName, required String modelName, String? clientToken, DataPreProcessingConfiguration? dataPreProcessingConfiguration, DatasetSchema? datasetSchema, DateTime? evaluationDataEndTime, DateTime? evaluationDataStartTime, LabelsInputConfiguration? labelsInputConfiguration, ModelDiagnosticsOutputConfiguration? modelDiagnosticsOutputConfiguration, String? offCondition, String? roleArn, String? serverSideKmsKeyId, List<Tag>? tags, DateTime? trainingDataEndTime, DateTime? trainingDataStartTime}) → Future<CreateModelResponse>
Creates a machine learning model for data inference.
createRetrainingScheduler({required String lookbackWindow, required String modelName, required String retrainingFrequency, String? clientToken, ModelPromoteMode? promoteMode, DateTime? retrainingStartDate}) → Future<CreateRetrainingSchedulerResponse>
Creates a retraining scheduler on the specified model.
deleteDataset({required String datasetName}) → Future<void>
Deletes a dataset and associated artifacts. The operation will check to see if any inference scheduler or data ingestion job is currently using the dataset, and if there isn't, the dataset, its metadata, and any associated data stored in S3 will be deleted. This does not affect any models that used this dataset for training and evaluation, but does prevent it from being used in the future.
deleteInferenceScheduler({required String inferenceSchedulerName}) → Future<void>
Deletes an inference scheduler that has been set up. Prior inference results will not be deleted.
deleteLabel({required String labelGroupName, required String labelId}) → Future<void>
Deletes a label.
deleteLabelGroup({required String labelGroupName}) → Future<void>
Deletes a group of labels.
deleteModel({required String modelName}) → Future<void>
Deletes a machine learning model currently available for Amazon Lookout for Equipment. This will prevent it from being used with an inference scheduler, even one that is already set up.
deleteResourcePolicy({required String resourceArn}) → Future<void>
Deletes the resource policy attached to the resource.
deleteRetrainingScheduler({required String modelName}) → Future<void>
Deletes a retraining scheduler from a model. The retraining scheduler must be in the STOPPED status.
describeDataIngestionJob({required String jobId}) → Future<DescribeDataIngestionJobResponse>
Provides information on a specific data ingestion job such as creation time, dataset ARN, and status.
describeDataset({required String datasetName}) → Future<DescribeDatasetResponse>
Provides a JSON description of the data in each time series dataset, including names, column names, and data types.
describeInferenceScheduler({required String inferenceSchedulerName}) → Future<DescribeInferenceSchedulerResponse>
Specifies information about the inference scheduler being used, including name, model, status, and associated metadata
describeLabel({required String labelGroupName, required String labelId}) → Future<DescribeLabelResponse>
Returns the name of the label.
describeLabelGroup({required String labelGroupName}) → Future<DescribeLabelGroupResponse>
Returns information about the label group.
describeModel({required String modelName}) → Future<DescribeModelResponse>
Provides a JSON containing the overall information about a specific machine learning model, including model name and ARN, dataset, training and evaluation information, status, and so on.
describeModelVersion({required String modelName, required int modelVersion}) → Future<DescribeModelVersionResponse>
Retrieves information about a specific machine learning model version.
describeResourcePolicy({required String resourceArn}) → Future<DescribeResourcePolicyResponse>
Provides the details of a resource policy attached to a resource.
describeRetrainingScheduler({required String modelName}) → Future<DescribeRetrainingSchedulerResponse>
Provides a description of the retraining scheduler, including information such as the model name and retraining parameters.
importDataset({required String sourceDatasetArn, String? clientToken, String? datasetName, String? serverSideKmsKeyId, List<Tag>? tags}) → Future<ImportDatasetResponse>
Imports a dataset.
importModelVersion({required String datasetName, required String sourceModelVersionArn, String? clientToken, InferenceDataImportStrategy? inferenceDataImportStrategy, LabelsInputConfiguration? labelsInputConfiguration, String? modelName, String? roleArn, String? serverSideKmsKeyId, List<Tag>? tags}) → Future<ImportModelVersionResponse>
Imports a model that has been trained successfully.
listDataIngestionJobs({String? datasetName, int? maxResults, String? nextToken, IngestionJobStatus? status}) → Future<ListDataIngestionJobsResponse>
Provides a list of all data ingestion jobs, including dataset name and ARN, S3 location of the input data, status, and so on.
listDatasets({String? datasetNameBeginsWith, int? maxResults, String? nextToken}) → Future<ListDatasetsResponse>
Lists all datasets currently available in your account, filtering on the dataset name.
listInferenceEvents({required String inferenceSchedulerName, required DateTime intervalEndTime, required DateTime intervalStartTime, int? maxResults, String? nextToken}) → Future<ListInferenceEventsResponse>
Lists all inference events that have been found for the specified inference scheduler.
listInferenceExecutions({required String inferenceSchedulerName, DateTime? dataEndTimeBefore, DateTime? dataStartTimeAfter, int? maxResults, String? nextToken, InferenceExecutionStatus? status}) → Future<ListInferenceExecutionsResponse>
Lists all inference executions that have been performed by the specified inference scheduler.
listInferenceSchedulers({String? inferenceSchedulerNameBeginsWith, int? maxResults, String? modelName, String? nextToken, InferenceSchedulerStatus? status}) → Future<ListInferenceSchedulersResponse>
Retrieves a list of all inference schedulers currently available for your account.
listLabelGroups({String? labelGroupNameBeginsWith, int? maxResults, String? nextToken}) → Future<ListLabelGroupsResponse>
Returns a list of the label groups.
listLabels({required String labelGroupName, String? equipment, String? faultCode, DateTime? intervalEndTime, DateTime? intervalStartTime, int? maxResults, String? nextToken}) → Future<ListLabelsResponse>
Provides a list of labels.
listModels({String? datasetNameBeginsWith, int? maxResults, String? modelNameBeginsWith, String? nextToken, ModelStatus? status}) → Future<ListModelsResponse>
Generates a list of all models in the account, including model name and ARN, dataset, and status.
listModelVersions({required String modelName, DateTime? createdAtEndTime, DateTime? createdAtStartTime, int? maxModelVersion, int? maxResults, int? minModelVersion, String? nextToken, ModelVersionSourceType? sourceType, ModelVersionStatus? status}) → Future<ListModelVersionsResponse>
Generates a list of all model versions for a given model, including the model version, model version ARN, and status. To list a subset of versions, use the MaxModelVersion and MinModelVersion fields.
listRetrainingSchedulers({int? maxResults, String? modelNameBeginsWith, String? nextToken, RetrainingSchedulerStatus? status}) → Future<ListRetrainingSchedulersResponse>
Lists all retraining schedulers in your account, filtering by model name prefix and status.
listSensorStatistics({required String datasetName, String? ingestionJobId, int? maxResults, String? nextToken}) → Future<ListSensorStatisticsResponse>
Lists statistics about the data collected for each of the sensors that have been successfully ingested in the particular dataset. Can also be used to retreive Sensor Statistics for a previous ingestion job.
listTagsForResource({required String resourceArn}) → Future<ListTagsForResourceResponse>
Lists all the tags for a specified resource, including key and value.
noSuchMethod(Invocation invocation) → dynamic
Invoked when a nonexistent method or property is accessed.
inherited
putResourcePolicy({required String resourceArn, required String resourcePolicy, String? clientToken, String? policyRevisionId}) → Future<PutResourcePolicyResponse>
Creates a resource control policy for a given resource.
startDataIngestionJob({required String datasetName, required IngestionInputConfiguration ingestionInputConfiguration, required String roleArn, String? clientToken}) → Future<StartDataIngestionJobResponse>
Starts a data ingestion job. Amazon Lookout for Equipment returns the job status.
startInferenceScheduler({required String inferenceSchedulerName}) → Future<StartInferenceSchedulerResponse>
Starts an inference scheduler.
startRetrainingScheduler({required String modelName}) → Future<StartRetrainingSchedulerResponse>
Starts a retraining scheduler.
stopInferenceScheduler({required String inferenceSchedulerName}) → Future<StopInferenceSchedulerResponse>
Stops an inference scheduler.
stopRetrainingScheduler({required String modelName}) → Future<StopRetrainingSchedulerResponse>
Stops a retraining scheduler.
tagResource({required String resourceArn, required List<Tag> tags}) → Future<void>
Associates a given tag to a resource in your account. A tag is a key-value pair which can be added to an Amazon Lookout for Equipment resource as metadata. Tags can be used for organizing your resources as well as helping you to search and filter by tag. Multiple tags can be added to a resource, either when you create it, or later. Up to 50 tags can be associated with each resource.
toString() → String
A string representation of this object.
inherited
untagResource({required String resourceArn, required List<String> tagKeys}) → Future<void>
Removes a specific tag from a given resource. The tag is specified by its key.
updateActiveModelVersion({required String modelName, required int modelVersion}) → Future<UpdateActiveModelVersionResponse>
Sets the active model version for a given machine learning model.
updateInferenceScheduler({required String inferenceSchedulerName, int? dataDelayOffsetInMinutes, InferenceInputConfiguration? dataInputConfiguration, InferenceOutputConfiguration? dataOutputConfiguration, DataUploadFrequency? dataUploadFrequency, String? roleArn}) → Future<void>
Updates an inference scheduler.
updateLabelGroup({required String labelGroupName, List<String>? faultCodes}) → Future<void>
Updates the label group.
updateModel({required String modelName, LabelsInputConfiguration? labelsInputConfiguration, ModelDiagnosticsOutputConfiguration? modelDiagnosticsOutputConfiguration, String? roleArn}) → Future<void>
Updates a model in the account.
updateRetrainingScheduler({required String modelName, String? lookbackWindow, ModelPromoteMode? promoteMode, String? retrainingFrequency, DateTime? retrainingStartDate}) → Future<void>
Updates a retraining scheduler.

Operators

operator ==(Object other) → bool
The equality operator.
inherited