LookoutEquipment

Service object for interacting with AWS LookoutEquipment service.

public struct LookoutEquipment: AWSService

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.

Inheritance

AWSService

Initializers

`init(client:region:partition:endpoint:timeout:byteBufferAllocator:options:)`

Initialize the LookoutEquipment client

public init(
        client: AWSClient,
        region: SotoCore.Region? = nil,
        partition: AWSPartition = .aws,
        endpoint: String? = nil,
        timeout: TimeAmount? = nil,
        byteBufferAllocator: ByteBufferAllocator = ByteBufferAllocator(),
        options: AWSServiceConfig.Options = []
    )

Parameters

client: AWSClient used to process requests
region: Region of server you want to communicate with. This will override the partition parameter.
partition: AWS partition where service resides, standard (.aws), china (.awscn), government (.awsusgov).
endpoint: Custom endpoint URL to use instead of standard AWS servers
timeout: Timeout value for HTTP requests

`init(from:patch:)`

Initializer required by AWSService.with(middlewares:timeout:byteBufferAllocator:options). You are not able to use this initializer directly as there are no public initializers for AWSServiceConfig.Patch. Please use AWSService.with(middlewares:timeout:byteBufferAllocator:options) instead.

public init(from: LookoutEquipment, patch: AWSServiceConfig.Patch)

Properties

`client`

Client used for communication with AWS

public let client: AWSClient

`config`

Service configuration

public let config: AWSServiceConfig

Methods

`createDataset(_:logger:on:)`

public func createDataset(_ input: CreateDatasetRequest, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil) -> EventLoopFuture<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. In other words, 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(_:logger:on:)`

public func createInferenceScheduler(_ input: CreateInferenceSchedulerRequest, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil) -> EventLoopFuture<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.

`createModel(_:logger:on:)`

public func createModel(_ input: CreateModelRequest, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil) -> EventLoopFuture<CreateModelResponse>

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.

`deleteDataset(_:logger:on:)`

@discardableResult public func deleteDataset(_ input: DeleteDatasetRequest, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil) -> EventLoopFuture<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(_:logger:on:)`

@discardableResult public func deleteInferenceScheduler(_ input: DeleteInferenceSchedulerRequest, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil) -> EventLoopFuture<Void>

Deletes an inference scheduler that has been set up. Already processed output results are not affected.

`deleteModel(_:logger:on:)`

@discardableResult public func deleteModel(_ input: DeleteModelRequest, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil) -> EventLoopFuture<Void>

Deletes an ML 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.

`describeDataIngestionJob(_:logger:on:)`

public func describeDataIngestionJob(_ input: DescribeDataIngestionJobRequest, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil) -> EventLoopFuture<DescribeDataIngestionJobResponse>

Provides information on a specific data ingestion job such as creation time, dataset ARN, and status.

`describeDataset(_:logger:on:)`

public func describeDataset(_ input: DescribeDatasetRequest, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil) -> EventLoopFuture<DescribeDatasetResponse>

Provides a JSON description of the data in each time series dataset, including names, column names, and data types.

`describeInferenceScheduler(_:logger:on:)`

public func describeInferenceScheduler(_ input: DescribeInferenceSchedulerRequest, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil) -> EventLoopFuture<DescribeInferenceSchedulerResponse>

Specifies information about the inference scheduler being used, including name, model, status, and associated metadata

`describeModel(_:logger:on:)`

public func describeModel(_ input: DescribeModelRequest, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil) -> EventLoopFuture<DescribeModelResponse>

Provides a JSON containing the overall information about a specific ML model, including model name and ARN, dataset, training and evaluation information, status, and so on.

`listDataIngestionJobs(_:logger:on:)`

public func listDataIngestionJobs(_ input: ListDataIngestionJobsRequest, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil) -> EventLoopFuture<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(_:logger:on:)`

public func listDatasets(_ input: ListDatasetsRequest, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil) -> EventLoopFuture<ListDatasetsResponse>

Lists all datasets currently available in your account, filtering on the dataset name.

`listInferenceExecutions(_:logger:on:)`

public func listInferenceExecutions(_ input: ListInferenceExecutionsRequest, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil) -> EventLoopFuture<ListInferenceExecutionsResponse>

Lists all inference executions that have been performed by the specified inference scheduler.

`listInferenceSchedulers(_:logger:on:)`

public func listInferenceSchedulers(_ input: ListInferenceSchedulersRequest, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil) -> EventLoopFuture<ListInferenceSchedulersResponse>

Retrieves a list of all inference schedulers currently available for your account.

`listModels(_:logger:on:)`

public func listModels(_ input: ListModelsRequest, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil) -> EventLoopFuture<ListModelsResponse>

Generates a list of all models in the account, including model name and ARN, dataset, and status.

`listSensorStatistics(_:logger:on:)`

public func listSensorStatistics(_ input: ListSensorStatisticsRequest, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil) -> EventLoopFuture<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(_:logger:on:)`

public func listTagsForResource(_ input: ListTagsForResourceRequest, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil) -> EventLoopFuture<ListTagsForResourceResponse>

Lists all the tags for a specified resource, including key and value.

`startDataIngestionJob(_:logger:on:)`

public func startDataIngestionJob(_ input: StartDataIngestionJobRequest, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil) -> EventLoopFuture<StartDataIngestionJobResponse>

Starts a data ingestion job. Amazon Lookout for Equipment returns the job status.

`startInferenceScheduler(_:logger:on:)`

public func startInferenceScheduler(_ input: StartInferenceSchedulerRequest, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil) -> EventLoopFuture<StartInferenceSchedulerResponse>

Starts an inference scheduler.

`stopInferenceScheduler(_:logger:on:)`

public func stopInferenceScheduler(_ input: StopInferenceSchedulerRequest, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil) -> EventLoopFuture<StopInferenceSchedulerResponse>

Stops an inference scheduler.

`tagResource(_:logger:on:)`

public func tagResource(_ input: TagResourceRequest, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil) -> EventLoopFuture<TagResourceResponse>

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.

`untagResource(_:logger:on:)`

public func untagResource(_ input: UntagResourceRequest, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil) -> EventLoopFuture<UntagResourceResponse>

Removes a specific tag from a given resource. The tag is specified by its key.

`updateInferenceScheduler(_:logger:on:)`

@discardableResult public func updateInferenceScheduler(_ input: UpdateInferenceSchedulerRequest, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil) -> EventLoopFuture<Void>

Updates an inference scheduler.

`listDataIngestionJobsPaginator(_:logger:on:)`

compiler(>=5.5.2) && canImport(_Concurrency)

public func listDataIngestionJobsPaginator(
        _ input: ListDataIngestionJobsRequest,
        logger: Logger = AWSClient.loggingDisabled,
        on eventLoop: EventLoop? = nil
    ) -> AWSClient.PaginatorSequence<ListDataIngestionJobsRequest, ListDataIngestionJobsResponse>

Provides a list of all data ingestion jobs, including dataset name and ARN, S3 location of the input data, status, and so on.

Return PaginatorSequence for operation. - Parameters: - input: Input for request - logger: Logger used flot logging - eventLoop: EventLoop to run this process on

`listDatasetsPaginator(_:logger:on:)`

compiler(>=5.5.2) && canImport(_Concurrency)

public func listDatasetsPaginator(
        _ input: ListDatasetsRequest,
        logger: Logger = AWSClient.loggingDisabled,
        on eventLoop: EventLoop? = nil
    ) -> AWSClient.PaginatorSequence<ListDatasetsRequest, ListDatasetsResponse>

Lists all datasets currently available in your account, filtering on the dataset name.

Return PaginatorSequence for operation. - Parameters: - input: Input for request - logger: Logger used flot logging - eventLoop: EventLoop to run this process on

`listInferenceExecutionsPaginator(_:logger:on:)`

compiler(>=5.5.2) && canImport(_Concurrency)

public func listInferenceExecutionsPaginator(
        _ input: ListInferenceExecutionsRequest,
        logger: Logger = AWSClient.loggingDisabled,
        on eventLoop: EventLoop? = nil
    ) -> AWSClient.PaginatorSequence<ListInferenceExecutionsRequest, ListInferenceExecutionsResponse>

Lists all inference executions that have been performed by the specified inference scheduler.

Return PaginatorSequence for operation. - Parameters: - input: Input for request - logger: Logger used flot logging - eventLoop: EventLoop to run this process on

`listInferenceSchedulersPaginator(_:logger:on:)`

compiler(>=5.5.2) && canImport(_Concurrency)

public func listInferenceSchedulersPaginator(
        _ input: ListInferenceSchedulersRequest,
        logger: Logger = AWSClient.loggingDisabled,
        on eventLoop: EventLoop? = nil
    ) -> AWSClient.PaginatorSequence<ListInferenceSchedulersRequest, ListInferenceSchedulersResponse>

Retrieves a list of all inference schedulers currently available for your account.

Return PaginatorSequence for operation. - Parameters: - input: Input for request - logger: Logger used flot logging - eventLoop: EventLoop to run this process on

`listModelsPaginator(_:logger:on:)`

compiler(>=5.5.2) && canImport(_Concurrency)

public func listModelsPaginator(
        _ input: ListModelsRequest,
        logger: Logger = AWSClient.loggingDisabled,
        on eventLoop: EventLoop? = nil
    ) -> AWSClient.PaginatorSequence<ListModelsRequest, ListModelsResponse>

Generates a list of all models in the account, including model name and ARN, dataset, and status.

Return PaginatorSequence for operation. - Parameters: - input: Input for request - logger: Logger used flot logging - eventLoop: EventLoop to run this process on

`listSensorStatisticsPaginator(_:logger:on:)`

compiler(>=5.5.2) && canImport(_Concurrency)

public func listSensorStatisticsPaginator(
        _ input: ListSensorStatisticsRequest,
        logger: Logger = AWSClient.loggingDisabled,
        on eventLoop: EventLoop? = nil
    ) -> AWSClient.PaginatorSequence<ListSensorStatisticsRequest, ListSensorStatisticsResponse>

Return PaginatorSequence for operation. - Parameters: - input: Input for request - logger: Logger used flot logging - eventLoop: EventLoop to run this process on

`listDataIngestionJobsPaginator(_:_:logger:on:onPage:)`

Provide paginated results to closure onPage for it to combine them into one result. This works in a similar manner to Array.reduce<Result>(_:_:) -> Result.

public func listDataIngestionJobsPaginator<Result>(
        _ input: ListDataIngestionJobsRequest,
        _ initialValue: Result,
        logger: Logger = AWSClient.loggingDisabled,
        on eventLoop: EventLoop? = nil,
        onPage: @escaping (Result, ListDataIngestionJobsResponse, EventLoop) -> EventLoopFuture<(Bool, Result)>
    ) -> EventLoopFuture<Result>

Provides a list of all data ingestion jobs, including dataset name and ARN, S3 location of the input data, status, and so on.

Parameters:

input: Input for request

initialValue: The value to use as the initial accumulating value. initialValue is passed to onPage the first time it is called.

logger: Logger used flot logging

eventLoop: EventLoop to run this process on

onPage: closure called with each paginated response. It combines an accumulating result with the contents of response. This combined result is then returned along with a boolean indicating if the paginate operation should continue.

`listDataIngestionJobsPaginator(_:logger:on:onPage:)`

Provide paginated results to closure onPage.

public func listDataIngestionJobsPaginator(
        _ input: ListDataIngestionJobsRequest,
        logger: Logger = AWSClient.loggingDisabled,
        on eventLoop: EventLoop? = nil,
        onPage: @escaping (ListDataIngestionJobsResponse, EventLoop) -> EventLoopFuture<Bool>
    ) -> EventLoopFuture<Void>

Parameters

input: Input for request
logger: Logger used flot logging
eventLoop: EventLoop to run this process on
onPage: closure called with each block of entries. Returns boolean indicating whether we should continue.

`listDatasetsPaginator(_:_:logger:on:onPage:)`

Provide paginated results to closure onPage for it to combine them into one result. This works in a similar manner to Array.reduce<Result>(_:_:) -> Result.

public func listDatasetsPaginator<Result>(
        _ input: ListDatasetsRequest,
        _ initialValue: Result,
        logger: Logger = AWSClient.loggingDisabled,
        on eventLoop: EventLoop? = nil,
        onPage: @escaping (Result, ListDatasetsResponse, EventLoop) -> EventLoopFuture<(Bool, Result)>
    ) -> EventLoopFuture<Result>

Lists all datasets currently available in your account, filtering on the dataset name.

Parameters:

input: Input for request

initialValue: The value to use as the initial accumulating value. initialValue is passed to onPage the first time it is called.

logger: Logger used flot logging

eventLoop: EventLoop to run this process on

onPage: closure called with each paginated response. It combines an accumulating result with the contents of response. This combined result is then returned along with a boolean indicating if the paginate operation should continue.

`listDatasetsPaginator(_:logger:on:onPage:)`

Provide paginated results to closure onPage.

public func listDatasetsPaginator(
        _ input: ListDatasetsRequest,
        logger: Logger = AWSClient.loggingDisabled,
        on eventLoop: EventLoop? = nil,
        onPage: @escaping (ListDatasetsResponse, EventLoop) -> EventLoopFuture<Bool>
    ) -> EventLoopFuture<Void>

Parameters

input: Input for request
logger: Logger used flot logging
eventLoop: EventLoop to run this process on
onPage: closure called with each block of entries. Returns boolean indicating whether we should continue.

`listInferenceExecutionsPaginator(_:_:logger:on:onPage:)`

Provide paginated results to closure onPage for it to combine them into one result. This works in a similar manner to Array.reduce<Result>(_:_:) -> Result.

public func listInferenceExecutionsPaginator<Result>(
        _ input: ListInferenceExecutionsRequest,
        _ initialValue: Result,
        logger: Logger = AWSClient.loggingDisabled,
        on eventLoop: EventLoop? = nil,
        onPage: @escaping (Result, ListInferenceExecutionsResponse, EventLoop) -> EventLoopFuture<(Bool, Result)>
    ) -> EventLoopFuture<Result>

Lists all inference executions that have been performed by the specified inference scheduler.

Parameters:

input: Input for request

initialValue: The value to use as the initial accumulating value. initialValue is passed to onPage the first time it is called.

logger: Logger used flot logging

eventLoop: EventLoop to run this process on

onPage: closure called with each paginated response. It combines an accumulating result with the contents of response. This combined result is then returned along with a boolean indicating if the paginate operation should continue.

`listInferenceExecutionsPaginator(_:logger:on:onPage:)`

Provide paginated results to closure onPage.

public func listInferenceExecutionsPaginator(
        _ input: ListInferenceExecutionsRequest,
        logger: Logger = AWSClient.loggingDisabled,
        on eventLoop: EventLoop? = nil,
        onPage: @escaping (ListInferenceExecutionsResponse, EventLoop) -> EventLoopFuture<Bool>
    ) -> EventLoopFuture<Void>

Parameters

input: Input for request
logger: Logger used flot logging
eventLoop: EventLoop to run this process on
onPage: closure called with each block of entries. Returns boolean indicating whether we should continue.

`listInferenceSchedulersPaginator(_:_:logger:on:onPage:)`

Provide paginated results to closure onPage for it to combine them into one result. This works in a similar manner to Array.reduce<Result>(_:_:) -> Result.

public func listInferenceSchedulersPaginator<Result>(
        _ input: ListInferenceSchedulersRequest,
        _ initialValue: Result,
        logger: Logger = AWSClient.loggingDisabled,
        on eventLoop: EventLoop? = nil,
        onPage: @escaping (Result, ListInferenceSchedulersResponse, EventLoop) -> EventLoopFuture<(Bool, Result)>
    ) -> EventLoopFuture<Result>

Retrieves a list of all inference schedulers currently available for your account.

Parameters:

input: Input for request

initialValue: The value to use as the initial accumulating value. initialValue is passed to onPage the first time it is called.

logger: Logger used flot logging

eventLoop: EventLoop to run this process on

onPage: closure called with each paginated response. It combines an accumulating result with the contents of response. This combined result is then returned along with a boolean indicating if the paginate operation should continue.

`listInferenceSchedulersPaginator(_:logger:on:onPage:)`

Provide paginated results to closure onPage.

public func listInferenceSchedulersPaginator(
        _ input: ListInferenceSchedulersRequest,
        logger: Logger = AWSClient.loggingDisabled,
        on eventLoop: EventLoop? = nil,
        onPage: @escaping (ListInferenceSchedulersResponse, EventLoop) -> EventLoopFuture<Bool>
    ) -> EventLoopFuture<Void>

Parameters

input: Input for request
logger: Logger used flot logging
eventLoop: EventLoop to run this process on
onPage: closure called with each block of entries. Returns boolean indicating whether we should continue.

`listModelsPaginator(_:_:logger:on:onPage:)`

Provide paginated results to closure onPage for it to combine them into one result. This works in a similar manner to Array.reduce<Result>(_:_:) -> Result.

public func listModelsPaginator<Result>(
        _ input: ListModelsRequest,
        _ initialValue: Result,
        logger: Logger = AWSClient.loggingDisabled,
        on eventLoop: EventLoop? = nil,
        onPage: @escaping (Result, ListModelsResponse, EventLoop) -> EventLoopFuture<(Bool, Result)>
    ) -> EventLoopFuture<Result>

Generates a list of all models in the account, including model name and ARN, dataset, and status.

Parameters:

input: Input for request

initialValue: The value to use as the initial accumulating value. initialValue is passed to onPage the first time it is called.

logger: Logger used flot logging

eventLoop: EventLoop to run this process on

onPage: closure called with each paginated response. It combines an accumulating result with the contents of response. This combined result is then returned along with a boolean indicating if the paginate operation should continue.

`listModelsPaginator(_:logger:on:onPage:)`

Provide paginated results to closure onPage.

public func listModelsPaginator(
        _ input: ListModelsRequest,
        logger: Logger = AWSClient.loggingDisabled,
        on eventLoop: EventLoop? = nil,
        onPage: @escaping (ListModelsResponse, EventLoop) -> EventLoopFuture<Bool>
    ) -> EventLoopFuture<Void>

Parameters

input: Input for request
logger: Logger used flot logging
eventLoop: EventLoop to run this process on
onPage: closure called with each block of entries. Returns boolean indicating whether we should continue.

`listSensorStatisticsPaginator(_:_:logger:on:onPage:)`

Provide paginated results to closure onPage for it to combine them into one result. This works in a similar manner to Array.reduce<Result>(_:_:) -> Result.

public func listSensorStatisticsPaginator<Result>(
        _ input: ListSensorStatisticsRequest,
        _ initialValue: Result,
        logger: Logger = AWSClient.loggingDisabled,
        on eventLoop: EventLoop? = nil,
        onPage: @escaping (Result, ListSensorStatisticsResponse, EventLoop) -> EventLoopFuture<(Bool, Result)>
    ) -> EventLoopFuture<Result>

Parameters:

input: Input for request

initialValue: The value to use as the initial accumulating value. initialValue is passed to onPage the first time it is called.

logger: Logger used flot logging

eventLoop: EventLoop to run this process on

onPage: closure called with each paginated response. It combines an accumulating result with the contents of response. This combined result is then returned along with a boolean indicating if the paginate operation should continue.

`listSensorStatisticsPaginator(_:logger:on:onPage:)`

Provide paginated results to closure onPage.

public func listSensorStatisticsPaginator(
        _ input: ListSensorStatisticsRequest,
        logger: Logger = AWSClient.loggingDisabled,
        on eventLoop: EventLoop? = nil,
        onPage: @escaping (ListSensorStatisticsResponse, EventLoop) -> EventLoopFuture<Bool>
    ) -> EventLoopFuture<Void>

Parameters

input: Input for request
logger: Logger used flot logging
eventLoop: EventLoop to run this process on
onPage: closure called with each block of entries. Returns boolean indicating whether we should continue.