@Generated(value="com.amazonaws:aws-java-sdk-code-generator") public class AbstractAmazonLookoutEquipment extends Object implements AmazonLookoutEquipment
AmazonLookoutEquipment. Convenient method forms pass through to the corresponding
overload that takes a request object, which throws an UnsupportedOperationException.ENDPOINT_PREFIX| Modifier and Type | Method and Description |
|---|---|
CreateDatasetResult |
createDataset(CreateDatasetRequest request)
Creates a container for a collection of data being ingested for analysis.
|
CreateInferenceSchedulerResult |
createInferenceScheduler(CreateInferenceSchedulerRequest request)
Creates a scheduled inference.
|
CreateModelResult |
createModel(CreateModelRequest request)
Creates an ML model for data inference.
|
DeleteDatasetResult |
deleteDataset(DeleteDatasetRequest request)
Deletes a dataset and associated artifacts.
|
DeleteInferenceSchedulerResult |
deleteInferenceScheduler(DeleteInferenceSchedulerRequest request)
Deletes an inference scheduler that has been set up.
|
DeleteModelResult |
deleteModel(DeleteModelRequest request)
Deletes an ML model currently available for Amazon Lookout for Equipment.
|
DescribeDataIngestionJobResult |
describeDataIngestionJob(DescribeDataIngestionJobRequest request)
Provides information on a specific data ingestion job such as creation time, dataset ARN, status, and so on.
|
DescribeDatasetResult |
describeDataset(DescribeDatasetRequest request)
Provides information on a specified dataset such as the schema location, status, and so on.
|
DescribeInferenceSchedulerResult |
describeInferenceScheduler(DescribeInferenceSchedulerRequest request)
Specifies information about the inference scheduler being used, including name, model, status, and associated
metadata
|
DescribeModelResult |
describeModel(DescribeModelRequest request)
Provides overall information about a specific ML model, including model name and ARN, dataset, training and
evaluation information, status, and so on.
|
ResponseMetadata |
getCachedResponseMetadata(AmazonWebServiceRequest request)
Returns additional metadata for a previously executed successful request, typically used for debugging issues
where a service isn't acting as expected.
|
ListDataIngestionJobsResult |
listDataIngestionJobs(ListDataIngestionJobsRequest request)
Provides a list of all data ingestion jobs, including dataset name and ARN, S3 location of the input data,
status, and so on.
|
ListDatasetsResult |
listDatasets(ListDatasetsRequest request)
Lists all datasets currently available in your account, filtering on the dataset name.
|
ListInferenceExecutionsResult |
listInferenceExecutions(ListInferenceExecutionsRequest request)
Lists all inference executions that have been performed by the specified inference scheduler.
|
ListInferenceSchedulersResult |
listInferenceSchedulers(ListInferenceSchedulersRequest request)
Retrieves a list of all inference schedulers currently available for your account.
|
ListModelsResult |
listModels(ListModelsRequest request)
Generates a list of all models in the account, including model name and ARN, dataset, and status.
|
ListTagsForResourceResult |
listTagsForResource(ListTagsForResourceRequest request)
Lists all the tags for a specified resource, including key and value.
|
void |
shutdown()
Shuts down this client object, releasing any resources that might be held open.
|
StartDataIngestionJobResult |
startDataIngestionJob(StartDataIngestionJobRequest request)
Starts a data ingestion job.
|
StartInferenceSchedulerResult |
startInferenceScheduler(StartInferenceSchedulerRequest request)
Starts an inference scheduler.
|
StopInferenceSchedulerResult |
stopInferenceScheduler(StopInferenceSchedulerRequest request)
Stops an inference scheduler.
|
TagResourceResult |
tagResource(TagResourceRequest request)
Associates a given tag to a resource in your account.
|
UntagResourceResult |
untagResource(UntagResourceRequest request)
Removes a specific tag from a given resource.
|
UpdateInferenceSchedulerResult |
updateInferenceScheduler(UpdateInferenceSchedulerRequest request)
Updates an inference scheduler.
|
public CreateDatasetResult createDataset(CreateDatasetRequest request)
AmazonLookoutEquipmentCreates 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.
createDataset in interface AmazonLookoutEquipmentpublic CreateInferenceSchedulerResult createInferenceScheduler(CreateInferenceSchedulerRequest request)
AmazonLookoutEquipmentCreates 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.
createInferenceScheduler in interface AmazonLookoutEquipmentpublic CreateModelResult createModel(CreateModelRequest request)
AmazonLookoutEquipmentCreates 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.
createModel in interface AmazonLookoutEquipmentpublic DeleteDatasetResult deleteDataset(DeleteDatasetRequest request)
AmazonLookoutEquipmentDeletes 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.
deleteDataset in interface AmazonLookoutEquipmentpublic DeleteInferenceSchedulerResult deleteInferenceScheduler(DeleteInferenceSchedulerRequest request)
AmazonLookoutEquipmentDeletes an inference scheduler that has been set up. Already processed output results are not affected.
deleteInferenceScheduler in interface AmazonLookoutEquipmentpublic DeleteModelResult deleteModel(DeleteModelRequest request)
AmazonLookoutEquipmentDeletes 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.
deleteModel in interface AmazonLookoutEquipmentpublic DescribeDataIngestionJobResult describeDataIngestionJob(DescribeDataIngestionJobRequest request)
AmazonLookoutEquipmentProvides information on a specific data ingestion job such as creation time, dataset ARN, status, and so on.
describeDataIngestionJob in interface AmazonLookoutEquipmentpublic DescribeDatasetResult describeDataset(DescribeDatasetRequest request)
AmazonLookoutEquipmentProvides information on a specified dataset such as the schema location, status, and so on.
describeDataset in interface AmazonLookoutEquipmentpublic DescribeInferenceSchedulerResult describeInferenceScheduler(DescribeInferenceSchedulerRequest request)
AmazonLookoutEquipmentSpecifies information about the inference scheduler being used, including name, model, status, and associated metadata
describeInferenceScheduler in interface AmazonLookoutEquipmentpublic DescribeModelResult describeModel(DescribeModelRequest request)
AmazonLookoutEquipmentProvides overall information about a specific ML model, including model name and ARN, dataset, training and evaluation information, status, and so on.
describeModel in interface AmazonLookoutEquipmentpublic ListDataIngestionJobsResult listDataIngestionJobs(ListDataIngestionJobsRequest request)
AmazonLookoutEquipmentProvides a list of all data ingestion jobs, including dataset name and ARN, S3 location of the input data, status, and so on.
listDataIngestionJobs in interface AmazonLookoutEquipmentpublic ListDatasetsResult listDatasets(ListDatasetsRequest request)
AmazonLookoutEquipmentLists all datasets currently available in your account, filtering on the dataset name.
listDatasets in interface AmazonLookoutEquipmentpublic ListInferenceExecutionsResult listInferenceExecutions(ListInferenceExecutionsRequest request)
AmazonLookoutEquipmentLists all inference executions that have been performed by the specified inference scheduler.
listInferenceExecutions in interface AmazonLookoutEquipmentpublic ListInferenceSchedulersResult listInferenceSchedulers(ListInferenceSchedulersRequest request)
AmazonLookoutEquipmentRetrieves a list of all inference schedulers currently available for your account.
listInferenceSchedulers in interface AmazonLookoutEquipmentpublic ListModelsResult listModels(ListModelsRequest request)
AmazonLookoutEquipmentGenerates a list of all models in the account, including model name and ARN, dataset, and status.
listModels in interface AmazonLookoutEquipmentpublic ListTagsForResourceResult listTagsForResource(ListTagsForResourceRequest request)
AmazonLookoutEquipmentLists all the tags for a specified resource, including key and value.
listTagsForResource in interface AmazonLookoutEquipmentpublic StartDataIngestionJobResult startDataIngestionJob(StartDataIngestionJobRequest request)
AmazonLookoutEquipmentStarts a data ingestion job. Amazon Lookout for Equipment returns the job status.
startDataIngestionJob in interface AmazonLookoutEquipmentpublic StartInferenceSchedulerResult startInferenceScheduler(StartInferenceSchedulerRequest request)
AmazonLookoutEquipmentStarts an inference scheduler.
startInferenceScheduler in interface AmazonLookoutEquipmentpublic StopInferenceSchedulerResult stopInferenceScheduler(StopInferenceSchedulerRequest request)
AmazonLookoutEquipmentStops an inference scheduler.
stopInferenceScheduler in interface AmazonLookoutEquipmentpublic TagResourceResult tagResource(TagResourceRequest request)
AmazonLookoutEquipmentAssociates 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.
tagResource in interface AmazonLookoutEquipmentpublic UntagResourceResult untagResource(UntagResourceRequest request)
AmazonLookoutEquipmentRemoves a specific tag from a given resource. The tag is specified by its key.
untagResource in interface AmazonLookoutEquipmentpublic UpdateInferenceSchedulerResult updateInferenceScheduler(UpdateInferenceSchedulerRequest request)
AmazonLookoutEquipmentUpdates an inference scheduler.
updateInferenceScheduler in interface AmazonLookoutEquipmentpublic void shutdown()
AmazonLookoutEquipmentshutdown in interface AmazonLookoutEquipmentpublic ResponseMetadata getCachedResponseMetadata(AmazonWebServiceRequest request)
AmazonLookoutEquipmentResponse metadata is only cached for a limited period of time, so if you need to access this extra diagnostic information for an executed request, you should use this method to retrieve it as soon as possible after executing a request.
getCachedResponseMetadata in interface AmazonLookoutEquipmentrequest - The originally executed request.