Awso_lookoutequipment.ValuesSourceval structure_to_value_aux :
('a * 'b option) list ->
f:(('a * 'b) list -> 'c) ->
[> `Structure of 'c ]val structure_to_wrapped_value :
wrapper:'a ->
response:'a ->
('b * 'c option) list ->
[> `Structure of ('a * [> `Structure of ('b * 'c) list ]) list ]The Amazon S3 location for the pointwise model diagnostics for an Amazon Lookout for Equipment model.
Specifies configuration information for the input data for the inference, including timestamp format and delimiter.
Specifies configuration information for the input data for the inference, including input data S3 location.
Specifies configuration information for the output results from the inference, including output S3 location.
Specifies S3 configuration information for the input data for the data ingestion job.
Entity that comprises information on categorical values in data.
Entity that comprises information of count and percentage.
Entity that comprises information on large gaps between consecutive timestamps in data.
Entity that comprises information on monotonic values in the data.
Entity that comprises information on operating modes in data.
Output configuration information for the pointwise model diagnostics for an Amazon Lookout for Equipment model.
Specifies configuration information for the input data for the inference, including Amazon S3 location of input data..
Specifies configuration information for the output results from for the inference, including KMS key ID and output S3 location.
Specifies configuration information for the input data for the data ingestion job, including input data S3 location.
Entity that comprises information on sensors that have sensor data completely missing.
Entity that comprises information on sensors that have shorter date range.
The location information (prefix and bucket name) for the s3 location being used for label data.
A tag is a key-value pair that can be added to a resource as metadata.
Summary of ingestion statistics like whether data exists, number of missing values, number of invalid values and so on related to the particular sensor.
Provides information about the specified retraining scheduler, including model name, status, start date, frequency, and lookback window.
Provides information about the specified machine learning model, including dataset and model names and ARNs, as well as status.
Contains information about the specific model version.
Information about the label.
Contains information about the label group.
Contains information about the specific inference scheduler, including data delay offset, model name and ARN, status, and so on.
Contains information about the specific inference execution, including input and output data configuration, inference scheduling information, status, and so on.
Contains information about the specific inference event, including start and end time, diagnostics information, event duration and so on.
Contains information about the specific data set, including name, ARN, and status.
Provides information about a specified data ingestion job, including dataset information, data ingestion configuration, and status.
Entity that comprises information abount duplicate timestamps in the dataset.
Entity that comprises aggregated information on sensors having insufficient data.
Entity that comprises aggregated information on sensors having insufficient data.
Entity that comprises aggregated information on sensors having missing data.
Entity that comprises information abount unsupported timestamps in the dataset.
Contains the configuration information for the S3 location being used to hold label data.
The request could not be completed because you do not have access to the resource.
The request could not be completed due to a conflict with the current state of the target resource.
Processing of the request has failed because of an unknown error, exception or failure.
The resource requested could not be found. Verify the resource ID and retry your request.
The request was denied due to request throttling.
The input fails to satisfy constraints specified by Amazon Lookout for Equipment or a related Amazon Web Services service that's being utilized.
Resource limitations have been exceeded.
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
DataQualitySummary gives aggregated statistics over all the sensors about a completed ingestion job. It primarily gives more information about statistics over different incorrect data like MissingCompleteSensorData, MissingSensorData, UnsupportedDateFormats, InsufficientSensorData, DuplicateTimeStamps.
Gives statistics about how many files have been ingested, and which files have not been ingested, for a particular ingestion job.
Provides information about the data schema used with the given dataset.
Updates a retraining scheduler.
Updates a model in the account.
Updates the label group.
Updates an inference scheduler.
Sets the active model version for a given machine learning model.
Sets the active model version for a given machine learning model.
Removes a specific tag from a given resource. The tag is specified by its key.
Removes a specific tag from a given resource. The tag is specified by its key.
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.
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.
Stops a retraining scheduler.
Stops a retraining scheduler.
Stops an inference scheduler.
Stops an inference scheduler.
Starts a retraining scheduler.
Starts a retraining scheduler.
Starts an inference scheduler.
Starts an inference scheduler.
Starts a data ingestion job. Amazon Lookout for Equipment returns the job status.
Starts a data ingestion job. Amazon Lookout for Equipment returns the job status.
Creates a resource control policy for a given resource.
Creates a resource control policy for a given resource.
Lists all the tags for a specified resource, including key and value.
Lists all the tags for a specified resource, including key and value.
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.
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.
Lists all retraining schedulers in your account, filtering by model name prefix and status.
Lists all retraining schedulers in your account, filtering by model name prefix and status.
Generates a list of all models in the account, including model name and ARN, dataset, and status.
Generates a list of all models in the account, including model name and ARN, dataset, and status.
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.
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.
Provides a list of labels.
Provides a list of labels.
Returns a list of the label groups.
Returns a list of the label groups.
Retrieves a list of all inference schedulers currently available for your account.
Retrieves a list of all inference schedulers currently available for your account.
Lists all inference executions that have been performed by the specified inference scheduler.
Lists all inference executions that have been performed by the specified inference scheduler.
Lists all inference events that have been found for the specified inference scheduler.
Lists all inference events that have been found for the specified inference scheduler.
Lists all datasets currently available in your account, filtering on the dataset name.
Lists all datasets currently available in your account, filtering on the dataset name.
Provides a list of all data ingestion jobs, including dataset name and ARN, S3 location of the input data, status, and so on.
Provides a list of all data ingestion jobs, including dataset name and ARN, S3 location of the input data, status, and so on.
Imports a model that has been trained successfully.
Imports a model that has been trained successfully.
Imports a dataset.
Imports a dataset.
Provides a description of the retraining scheduler, including information such as the model name and retraining parameters.
Provides a description of the retraining scheduler, including information such as the model name and retraining parameters.
Provides the details of a resource policy attached to a resource.
Provides the details of a resource policy attached to a resource.
Retrieves information about a specific machine learning model version.
Retrieves information about a specific machine learning model version.
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.
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.
Returns the name of the label.
Returns the name of the label.
Returns information about the label group.
Returns information about the label group.
Specifies information about the inference scheduler being used, including name, model, status, and associated metadata
Specifies information about the inference scheduler being used, including name, model, status, and associated metadata
Provides a JSON description of the data in each time series dataset, including names, column names, and data types.
Provides a JSON description of the data in each time series dataset, including names, column names, and data types.
Provides information on a specific data ingestion job such as creation time, dataset ARN, and status.
Provides information on a specific data ingestion job such as creation time, dataset ARN, and status.
Deletes a retraining scheduler from a model. The retraining scheduler must be in the STOPPED status.
Deletes the resource policy attached to the resource.
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.
Deletes a label.
Deletes a group of labels.
Deletes an inference scheduler that has been set up. Prior inference results will not be deleted.
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.
Creates a retraining scheduler on the specified model.
Creates a retraining scheduler on the specified model.
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.
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.
Creates a label for an event.
Creates a label for an event.
Creates a group of labels.
Creates a group of labels.
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.
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.
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.
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.