View Source aws_lookoutequipment (aws v0.7.14)

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.

Link to this section Summary

Functions

Creates a container for a collection of data being ingested for analysis.

Creates a scheduled inference.

Creates a label for an event.
Creates a group of labels.

Creates an ML model for data inference.

Deletes a dataset and associated artifacts.

Deletes an inference scheduler that has been set up.

Deletes a label.
Deletes a group of labels.

Deletes an ML model currently available for Amazon Lookout for Equipment.

Provides information on a specific data ingestion job such as creation time, dataset ARN, and status.
Provides a JSON description of the data in each time series dataset, including names, column names, and data types.
Specifies information about the inference scheduler being used, including name, model, status, and associated metadata
Returns the name of the label.
Returns information about the label group.
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.
Provides a list of all data ingestion jobs, including dataset name and ARN, S3 location of the input data, status, and so on.
Lists all datasets currently available in your account, filtering on the dataset name.
Lists all inference events that have been found for the specified inference scheduler.
Lists all inference executions that have been performed by the specified inference scheduler.
Retrieves a list of all inference schedulers currently available for your account.
Returns a list of the label groups.
Provides a list of labels.
Generates a list of all models in the account, including model name and ARN, dataset, and status.

Lists statistics about the data collected for each of the sensors that have been successfully ingested in the particular dataset.

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

Starts a data ingestion job.

Starts an inference scheduler.
Stops an inference scheduler.

Associates a given tag to a resource in your account.

Removes a specific tag from a given resource.

Updates an inference scheduler.
Updates the label group.

Link to this section Functions

Link to this function

create_dataset(Client, Input)

View Source

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.
Link to this function

create_dataset(Client, Input, Options)

View Source
Link to this function

create_inference_scheduler(Client, Input)

View Source

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.
Link to this function

create_inference_scheduler(Client, Input, Options)

View Source
Link to this function

create_label(Client, Input)

View Source
Creates a label for an event.
Link to this function

create_label(Client, Input, Options)

View Source
Link to this function

create_label_group(Client, Input)

View Source
Creates a group of labels.
Link to this function

create_label_group(Client, Input, Options)

View Source
Link to this function

create_model(Client, Input)

View Source

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.
Link to this function

create_model(Client, Input, Options)

View Source
Link to this function

delete_dataset(Client, Input)

View Source

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.
Link to this function

delete_dataset(Client, Input, Options)

View Source
Link to this function

delete_inference_scheduler(Client, Input)

View Source

Deletes an inference scheduler that has been set up.

Already processed output results are not affected.
Link to this function

delete_inference_scheduler(Client, Input, Options)

View Source
Link to this function

delete_label(Client, Input)

View Source
Deletes a label.
Link to this function

delete_label(Client, Input, Options)

View Source
Link to this function

delete_label_group(Client, Input)

View Source
Deletes a group of labels.
Link to this function

delete_label_group(Client, Input, Options)

View Source
Link to this function

delete_model(Client, Input)

View Source

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.
Link to this function

delete_model(Client, Input, Options)

View Source
Link to this function

describe_data_ingestion_job(Client, Input)

View Source
Provides information on a specific data ingestion job such as creation time, dataset ARN, and status.
Link to this function

describe_data_ingestion_job(Client, Input, Options)

View Source
Link to this function

describe_dataset(Client, Input)

View Source
Provides a JSON description of the data in each time series dataset, including names, column names, and data types.
Link to this function

describe_dataset(Client, Input, Options)

View Source
Link to this function

describe_inference_scheduler(Client, Input)

View Source
Specifies information about the inference scheduler being used, including name, model, status, and associated metadata
Link to this function

describe_inference_scheduler(Client, Input, Options)

View Source
Link to this function

describe_label(Client, Input)

View Source
Returns the name of the label.
Link to this function

describe_label(Client, Input, Options)

View Source
Link to this function

describe_label_group(Client, Input)

View Source
Returns information about the label group.
Link to this function

describe_label_group(Client, Input, Options)

View Source
Link to this function

describe_model(Client, Input)

View Source
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.
Link to this function

describe_model(Client, Input, Options)

View Source
Link to this function

list_data_ingestion_jobs(Client, Input)

View Source
Provides a list of all data ingestion jobs, including dataset name and ARN, S3 location of the input data, status, and so on.
Link to this function

list_data_ingestion_jobs(Client, Input, Options)

View Source
Link to this function

list_datasets(Client, Input)

View Source
Lists all datasets currently available in your account, filtering on the dataset name.
Link to this function

list_datasets(Client, Input, Options)

View Source
Link to this function

list_inference_events(Client, Input)

View Source
Lists all inference events that have been found for the specified inference scheduler.
Link to this function

list_inference_events(Client, Input, Options)

View Source
Link to this function

list_inference_executions(Client, Input)

View Source
Lists all inference executions that have been performed by the specified inference scheduler.
Link to this function

list_inference_executions(Client, Input, Options)

View Source
Link to this function

list_inference_schedulers(Client, Input)

View Source
Retrieves a list of all inference schedulers currently available for your account.
Link to this function

list_inference_schedulers(Client, Input, Options)

View Source
Link to this function

list_label_groups(Client, Input)

View Source
Returns a list of the label groups.
Link to this function

list_label_groups(Client, Input, Options)

View Source
Link to this function

list_labels(Client, Input)

View Source
Provides a list of labels.
Link to this function

list_labels(Client, Input, Options)

View Source
Link to this function

list_models(Client, Input)

View Source
Generates a list of all models in the account, including model name and ARN, dataset, and status.
Link to this function

list_models(Client, Input, Options)

View Source
Link to this function

list_sensor_statistics(Client, Input)

View Source

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.
Link to this function

list_sensor_statistics(Client, Input, Options)

View Source
Link to this function

list_tags_for_resource(Client, Input)

View Source
Lists all the tags for a specified resource, including key and value.
Link to this function

list_tags_for_resource(Client, Input, Options)

View Source
Link to this function

start_data_ingestion_job(Client, Input)

View Source

Starts a data ingestion job.

Amazon Lookout for Equipment returns the job status.
Link to this function

start_data_ingestion_job(Client, Input, Options)

View Source
Link to this function

start_inference_scheduler(Client, Input)

View Source
Starts an inference scheduler.
Link to this function

start_inference_scheduler(Client, Input, Options)

View Source
Link to this function

stop_inference_scheduler(Client, Input)

View Source
Stops an inference scheduler.
Link to this function

stop_inference_scheduler(Client, Input, Options)

View Source
Link to this function

tag_resource(Client, Input)

View Source

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.
Link to this function

tag_resource(Client, Input, Options)

View Source
Link to this function

untag_resource(Client, Input)

View Source

Removes a specific tag from a given resource.

The tag is specified by its key.
Link to this function

untag_resource(Client, Input, Options)

View Source
Link to this function

update_inference_scheduler(Client, Input)

View Source
Updates an inference scheduler.
Link to this function

update_inference_scheduler(Client, Input, Options)

View Source
Link to this function

update_label_group(Client, Input)

View Source
Updates the label group.
Link to this function

update_label_group(Client, Input, Options)

View Source