2021/09/09 - Amazon Lookout for Equipment - 2 updated api methods
Changes Added OffCondition parameter to CreateModel API
{'OffCondition': 'string'}
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.
See also: AWS API Documentation
Request Syntax
client.create_model(
ModelName='string',
DatasetName='string',
DatasetSchema={
'InlineDataSchema': 'string'
},
LabelsInputConfiguration={
'S3InputConfiguration': {
'Bucket': 'string',
'Prefix': 'string'
}
},
ClientToken='string',
TrainingDataStartTime=datetime(2015, 1, 1),
TrainingDataEndTime=datetime(2015, 1, 1),
EvaluationDataStartTime=datetime(2015, 1, 1),
EvaluationDataEndTime=datetime(2015, 1, 1),
RoleArn='string',
DataPreProcessingConfiguration={
'TargetSamplingRate': 'PT1S'|'PT5S'|'PT10S'|'PT15S'|'PT30S'|'PT1M'|'PT5M'|'PT10M'|'PT15M'|'PT30M'|'PT1H'
},
ServerSideKmsKeyId='string',
Tags=[
{
'Key': 'string',
'Value': 'string'
},
],
OffCondition='string'
)
string
[REQUIRED]
The name for the ML model to be created.
string
[REQUIRED]
The name of the dataset for the ML model being created.
dict
The data schema for the ML model being created.
InlineDataSchema (string) --
dict
The input configuration for the labels being used for the ML model that's being created.
S3InputConfiguration (dict) -- [REQUIRED]
Contains location information for the S3 location being used for label data.
Bucket (string) -- [REQUIRED]
The name of the S3 bucket holding the label data.
Prefix (string) --
The prefix for the S3 bucket used for the label data.
string
[REQUIRED]
A unique identifier for the request. If you do not set the client request token, Amazon Lookout for Equipment generates one.
This field is autopopulated if not provided.
datetime
Indicates the time reference in the dataset that should be used to begin the subset of training data for the ML model.
datetime
Indicates the time reference in the dataset that should be used to end the subset of training data for the ML model.
datetime
Indicates the time reference in the dataset that should be used to begin the subset of evaluation data for the ML model.
datetime
Indicates the time reference in the dataset that should be used to end the subset of evaluation data for the ML model.
string
The Amazon Resource Name (ARN) of a role with permission to access the data source being used to create the ML model.
dict
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
TargetSamplingRate (string) --
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
string
Provides the identifier of the KMS key used to encrypt model data by Amazon Lookout for Equipment.
list
Any tags associated with the ML model being created.
(dict) --
A tag is a key-value pair that can be added to a resource as metadata.
Key (string) -- [REQUIRED]
The key for the specified tag.
Value (string) -- [REQUIRED]
The value for the specified tag.
string
Indicates that the asset associated with this sensor has been shut off. As long as this condition is met, Lookout for Equipment will not use data from this asset for training, evaluation, or inference.
dict
Response Syntax
{
'ModelArn': 'string',
'Status': 'IN_PROGRESS'|'SUCCESS'|'FAILED'
}
Response Structure
(dict) --
ModelArn (string) --
The Amazon Resource Name (ARN) of the model being created.
Status (string) --
Indicates the status of the CreateModel operation.
{'OffCondition': 'string'}
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.
See also: AWS API Documentation
Request Syntax
client.describe_model(
ModelName='string'
)
string
[REQUIRED]
The name of the ML model to be described.
dict
Response Syntax
{
'ModelName': 'string',
'ModelArn': 'string',
'DatasetName': 'string',
'DatasetArn': 'string',
'Schema': 'string',
'LabelsInputConfiguration': {
'S3InputConfiguration': {
'Bucket': 'string',
'Prefix': 'string'
}
},
'TrainingDataStartTime': datetime(2015, 1, 1),
'TrainingDataEndTime': datetime(2015, 1, 1),
'EvaluationDataStartTime': datetime(2015, 1, 1),
'EvaluationDataEndTime': datetime(2015, 1, 1),
'RoleArn': 'string',
'DataPreProcessingConfiguration': {
'TargetSamplingRate': 'PT1S'|'PT5S'|'PT10S'|'PT15S'|'PT30S'|'PT1M'|'PT5M'|'PT10M'|'PT15M'|'PT30M'|'PT1H'
},
'Status': 'IN_PROGRESS'|'SUCCESS'|'FAILED',
'TrainingExecutionStartTime': datetime(2015, 1, 1),
'TrainingExecutionEndTime': datetime(2015, 1, 1),
'FailedReason': 'string',
'ModelMetrics': 'string',
'LastUpdatedTime': datetime(2015, 1, 1),
'CreatedAt': datetime(2015, 1, 1),
'ServerSideKmsKeyId': 'string',
'OffCondition': 'string'
}
Response Structure
(dict) --
ModelName (string) --
The name of the ML model being described.
ModelArn (string) --
The Amazon Resource Name (ARN) of the ML model being described.
DatasetName (string) --
The name of the dataset being used by the ML being described.
DatasetArn (string) --
The Amazon Resouce Name (ARN) of the dataset used to create the ML model being described.
Schema (string) --
A JSON description of the data that is in each time series dataset, including names, column names, and data types.
LabelsInputConfiguration (dict) --
Specifies configuration information about the labels input, including its S3 location.
S3InputConfiguration (dict) --
Contains location information for the S3 location being used for label data.
Bucket (string) --
The name of the S3 bucket holding the label data.
Prefix (string) --
The prefix for the S3 bucket used for the label data.
TrainingDataStartTime (datetime) --
Indicates the time reference in the dataset that was used to begin the subset of training data for the ML model.
TrainingDataEndTime (datetime) --
Indicates the time reference in the dataset that was used to end the subset of training data for the ML model.
EvaluationDataStartTime (datetime) --
Indicates the time reference in the dataset that was used to begin the subset of evaluation data for the ML model.
EvaluationDataEndTime (datetime) --
Indicates the time reference in the dataset that was used to end the subset of evaluation data for the ML model.
RoleArn (string) --
The Amazon Resource Name (ARN) of a role with permission to access the data source for the ML model being described.
DataPreProcessingConfiguration (dict) --
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
TargetSamplingRate (string) --
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
Status (string) --
Specifies the current status of the model being described. Status describes the status of the most recent action of the model.
TrainingExecutionStartTime (datetime) --
Indicates the time at which the training of the ML model began.
TrainingExecutionEndTime (datetime) --
Indicates the time at which the training of the ML model was completed.
FailedReason (string) --
If the training of the ML model failed, this indicates the reason for that failure.
ModelMetrics (string) --
The Model Metrics show an aggregated summary of the model's performance within the evaluation time range. This is the JSON content of the metrics created when evaluating the model.
LastUpdatedTime (datetime) --
Indicates the last time the ML model was updated. The type of update is not specified.
CreatedAt (datetime) --
Indicates the time and date at which the ML model was created.
ServerSideKmsKeyId (string) --
Provides the identifier of the KMS key used to encrypt model data by Amazon Lookout for Equipment.
OffCondition (string) --
Indicates that the asset associated with this sensor has been shut off. As long as this condition is met, Lookout for Equipment will not use data from this asset for training, evaluation, or inference.