2022/08/30 - Amazon Lookout for Equipment - 9 new4 updated api methods
Changes This release adds new apis for providing labels.
Creates a group of labels.
See also: AWS API Documentation
Request Syntax
client.create_label_group(
LabelGroupName='string',
FaultCodes=[
'string',
],
ClientToken='string',
Tags=[
{
'Key': 'string',
'Value': 'string'
},
]
)
string
[REQUIRED]
Names a group of labels.
Data in this field will be retained for service usage. Follow best practices for the security of your data.
list
The acceptable fault codes (indicating the type of anomaly associated with the label) that can be used with this label group.
Data in this field will be retained for service usage. Follow best practices for the security of your data.
(string) --
string
[REQUIRED]
A unique identifier for the request to create a label group. If you do not set the client request token, Lookout for Equipment generates one.
This field is autopopulated if not provided.
list
Tags that provide metadata about the label group you are creating.
Data in this field will be retained for service usage. Follow best practices for the security of your data.
(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.
dict
Response Syntax
{
'LabelGroupName': 'string',
'LabelGroupArn': 'string'
}
Response Structure
(dict) --
LabelGroupName (string) --
The name of the label group that you have created. Data in this field will be retained for service usage. Follow best practices for the security of your data.
LabelGroupArn (string) --
The ARN of the label group that you have created.
Updates the label group.
See also: AWS API Documentation
Request Syntax
client.update_label_group(
LabelGroupName='string',
FaultCodes=[
'string',
]
)
string
[REQUIRED]
The name of the label group to be updated.
list
Updates the code indicating the type of anomaly associated with the label.
Data in this field will be retained for service usage. Follow best practices for the security of your data.
(string) --
None
Deletes a label.
See also: AWS API Documentation
Request Syntax
client.delete_label(
LabelGroupName='string',
LabelId='string'
)
string
[REQUIRED]
The name of the label group that contains the label that you want to delete. Data in this field will be retained for service usage. Follow best practices for the security of your data.
string
[REQUIRED]
The ID of the label that you want to delete.
None
Returns the name of the label.
See also: AWS API Documentation
Request Syntax
client.describe_label(
LabelGroupName='string',
LabelId='string'
)
string
[REQUIRED]
Returns the name of the group containing the label.
string
[REQUIRED]
Returns the ID of the label.
dict
Response Syntax
{
'LabelGroupName': 'string',
'LabelGroupArn': 'string',
'LabelId': 'string',
'StartTime': datetime(2015, 1, 1),
'EndTime': datetime(2015, 1, 1),
'Rating': 'ANOMALY'|'NO_ANOMALY'|'NEUTRAL',
'FaultCode': 'string',
'Notes': 'string',
'Equipment': 'string',
'CreatedAt': datetime(2015, 1, 1)
}
Response Structure
(dict) --
LabelGroupName (string) --
The name of the requested label group.
LabelGroupArn (string) --
The ARN of the requested label group.
LabelId (string) --
The ID of the requested label.
StartTime (datetime) --
The start time of the requested label.
EndTime (datetime) --
The end time of the requested label.
Rating (string) --
Indicates whether a labeled event represents an anomaly.
FaultCode (string) --
Indicates the type of anomaly associated with the label.
Data in this field will be retained for service usage. Follow best practices for the security of your data.
Notes (string) --
Metadata providing additional information about the label.
Data in this field will be retained for service usage. Follow best practices for the security of your data.
Equipment (string) --
Indicates that a label pertains to a particular piece of equipment.
CreatedAt (datetime) --
The time at which the label was created.
Deletes a group of labels.
See also: AWS API Documentation
Request Syntax
client.delete_label_group(
LabelGroupName='string'
)
string
[REQUIRED]
The name of the label group that you want to delete. Data in this field will be retained for service usage. Follow best practices for the security of your data.
None
Returns a list of the label groups.
See also: AWS API Documentation
Request Syntax
client.list_label_groups(
LabelGroupNameBeginsWith='string',
NextToken='string',
MaxResults=123
)
string
The beginning of the name of the label groups to be listed.
string
An opaque pagination token indicating where to continue the listing of label groups.
integer
Specifies the maximum number of label groups to list.
dict
Response Syntax
{
'NextToken': 'string',
'LabelGroupSummaries': [
{
'LabelGroupName': 'string',
'LabelGroupArn': 'string',
'CreatedAt': datetime(2015, 1, 1),
'UpdatedAt': datetime(2015, 1, 1)
},
]
}
Response Structure
(dict) --
NextToken (string) --
An opaque pagination token indicating where to continue the listing of label groups.
LabelGroupSummaries (list) --
A summary of the label groups.
(dict) --
Contains information about the label group.
LabelGroupName (string) --
The name of the label group.
LabelGroupArn (string) --
The ARN of the label group.
CreatedAt (datetime) --
The time at which the label group was created.
UpdatedAt (datetime) --
The time at which the label group was updated.
Returns information about the label group.
See also: AWS API Documentation
Request Syntax
client.describe_label_group(
LabelGroupName='string'
)
string
[REQUIRED]
Returns the name of the label group.
dict
Response Syntax
{
'LabelGroupName': 'string',
'LabelGroupArn': 'string',
'FaultCodes': [
'string',
],
'CreatedAt': datetime(2015, 1, 1),
'UpdatedAt': datetime(2015, 1, 1)
}
Response Structure
(dict) --
LabelGroupName (string) --
The name of the label group.
LabelGroupArn (string) --
The ARN of the label group.
FaultCodes (list) --
Codes indicating the type of anomaly associated with the labels in the lagbel group.
(string) --
CreatedAt (datetime) --
The time at which the label group was created.
UpdatedAt (datetime) --
The time at which the label group was updated.
Creates a label for an event.
See also: AWS API Documentation
Request Syntax
client.create_label(
LabelGroupName='string',
StartTime=datetime(2015, 1, 1),
EndTime=datetime(2015, 1, 1),
Rating='ANOMALY'|'NO_ANOMALY'|'NEUTRAL',
FaultCode='string',
Notes='string',
Equipment='string',
ClientToken='string'
)
string
[REQUIRED]
The name of a group of labels.
Data in this field will be retained for service usage. Follow best practices for the security of your data.
datetime
[REQUIRED]
The start time of the labeled event.
datetime
[REQUIRED]
The end time of the labeled event.
string
[REQUIRED]
Indicates whether a labeled event represents an anomaly.
string
Provides additional information about the label. The fault code must be defined in the FaultCodes attribute of the label group.
Data in this field will be retained for service usage. Follow best practices for the security of your data.
string
Metadata providing additional information about the label.
Data in this field will be retained for service usage. Follow best practices for the security of your data.
string
Indicates that a label pertains to a particular piece of equipment.
Data in this field will be retained for service usage. Follow best practices for the security of your data.
string
[REQUIRED]
A unique identifier for the request to create a label. If you do not set the client request token, Lookout for Equipment generates one.
This field is autopopulated if not provided.
dict
Response Syntax
{
'LabelId': 'string'
}
Response Structure
(dict) --
LabelId (string) --
The ID of the label that you have created.
Provides a list of labels.
See also: AWS API Documentation
Request Syntax
client.list_labels(
LabelGroupName='string',
IntervalStartTime=datetime(2015, 1, 1),
IntervalEndTime=datetime(2015, 1, 1),
FaultCode='string',
Equipment='string',
NextToken='string',
MaxResults=123
)
string
[REQUIRED]
Retruns the name of the label group.
datetime
Returns all the labels with a end time equal to or later than the start time given.
datetime
Returns all labels with a start time earlier than the end time given.
string
Returns labels with a particular fault code.
string
Lists the labels that pertain to a particular piece of equipment.
string
An opaque pagination token indicating where to continue the listing of label groups.
integer
Specifies the maximum number of labels to list.
dict
Response Syntax
{
'NextToken': 'string',
'LabelSummaries': [
{
'LabelGroupName': 'string',
'LabelId': 'string',
'LabelGroupArn': 'string',
'StartTime': datetime(2015, 1, 1),
'EndTime': datetime(2015, 1, 1),
'Rating': 'ANOMALY'|'NO_ANOMALY'|'NEUTRAL',
'FaultCode': 'string',
'Equipment': 'string',
'CreatedAt': datetime(2015, 1, 1)
},
]
}
Response Structure
(dict) --
NextToken (string) --
An opaque pagination token indicating where to continue the listing of datasets.
LabelSummaries (list) --
A summary of the items in the label group.
(dict) --
Information about the label.
LabelGroupName (string) --
The name of the label group.
LabelId (string) --
The ID of the label.
LabelGroupArn (string) --
The ARN of the label group.
StartTime (datetime) --
The timestamp indicating the start of the label.
EndTime (datetime) --
The timestamp indicating the end of the label.
Rating (string) --
Indicates whether a labeled event represents an anomaly.
FaultCode (string) --
Indicates the type of anomaly associated with the label.
Data in this field will be retained for service usage. Follow best practices for the security of your data.
Equipment (string) --
Indicates that a label pertains to a particular piece of equipment.
CreatedAt (datetime) --
The time at which the label was created.
{'LabelsInputConfiguration': {'LabelGroupName': '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'
},
'LabelGroupName': '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) --
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.
LabelGroupName (string) --
The name of the label group to be used for 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.
{'LatestInferenceResult': 'ANOMALOUS | NORMAL'}
Specifies information about the inference scheduler being used, including name, model, status, and associated metadata
See also: AWS API Documentation
Request Syntax
client.describe_inference_scheduler(
InferenceSchedulerName='string'
)
string
[REQUIRED]
The name of the inference scheduler being described.
dict
Response Syntax
{
'ModelArn': 'string',
'ModelName': 'string',
'InferenceSchedulerName': 'string',
'InferenceSchedulerArn': 'string',
'Status': 'PENDING'|'RUNNING'|'STOPPING'|'STOPPED',
'DataDelayOffsetInMinutes': 123,
'DataUploadFrequency': 'PT5M'|'PT10M'|'PT15M'|'PT30M'|'PT1H',
'CreatedAt': datetime(2015, 1, 1),
'UpdatedAt': datetime(2015, 1, 1),
'DataInputConfiguration': {
'S3InputConfiguration': {
'Bucket': 'string',
'Prefix': 'string'
},
'InputTimeZoneOffset': 'string',
'InferenceInputNameConfiguration': {
'TimestampFormat': 'string',
'ComponentTimestampDelimiter': 'string'
}
},
'DataOutputConfiguration': {
'S3OutputConfiguration': {
'Bucket': 'string',
'Prefix': 'string'
},
'KmsKeyId': 'string'
},
'RoleArn': 'string',
'ServerSideKmsKeyId': 'string',
'LatestInferenceResult': 'ANOMALOUS'|'NORMAL'
}
Response Structure
(dict) --
ModelArn (string) --
The Amazon Resource Name (ARN) of the ML model of the inference scheduler being described.
ModelName (string) --
The name of the ML model of the inference scheduler being described.
InferenceSchedulerName (string) --
The name of the inference scheduler being described.
InferenceSchedulerArn (string) --
The Amazon Resource Name (ARN) of the inference scheduler being described.
Status (string) --
Indicates the status of the inference scheduler.
DataDelayOffsetInMinutes (integer) --
A period of time (in minutes) by which inference on the data is delayed after the data starts. For instance, if you select an offset delay time of five minutes, inference will not begin on the data until the first data measurement after the five minute mark. For example, if five minutes is selected, the inference scheduler will wake up at the configured frequency with the additional five minute delay time to check the customer S3 bucket. The customer can upload data at the same frequency and they don't need to stop and restart the scheduler when uploading new data.
DataUploadFrequency (string) --
Specifies how often data is uploaded to the source S3 bucket for the input data. This value is the length of time between data uploads. For instance, if you select 5 minutes, Amazon Lookout for Equipment will upload the real-time data to the source bucket once every 5 minutes. This frequency also determines how often Amazon Lookout for Equipment starts a scheduled inference on your data. In this example, it starts once every 5 minutes.
CreatedAt (datetime) --
Specifies the time at which the inference scheduler was created.
UpdatedAt (datetime) --
Specifies the time at which the inference scheduler was last updated, if it was.
DataInputConfiguration (dict) --
Specifies configuration information for the input data for the inference scheduler, including delimiter, format, and dataset location.
S3InputConfiguration (dict) --
Specifies configuration information for the input data for the inference, including Amazon S3 location of input data.
Bucket (string) --
The bucket containing the input dataset for the inference.
Prefix (string) --
The prefix for the S3 bucket used for the input data for the inference.
InputTimeZoneOffset (string) --
Indicates the difference between your time zone and Coordinated Universal Time (UTC).
InferenceInputNameConfiguration (dict) --
Specifies configuration information for the input data for the inference, including timestamp format and delimiter.
TimestampFormat (string) --
The format of the timestamp, whether Epoch time, or standard, with or without hyphens (-).
ComponentTimestampDelimiter (string) --
Indicates the delimiter character used between items in the data.
DataOutputConfiguration (dict) --
Specifies information for the output results for the inference scheduler, including the output S3 location.
S3OutputConfiguration (dict) --
Specifies configuration information for the output results from for the inference, output S3 location.
Bucket (string) --
The bucket containing the output results from the inference
Prefix (string) --
The prefix for the S3 bucket used for the output results from the inference.
KmsKeyId (string) --
The ID number for the AWS KMS key used to encrypt the inference output.
RoleArn (string) --
The Amazon Resource Name (ARN) of a role with permission to access the data source for the inference scheduler being described.
ServerSideKmsKeyId (string) --
Provides the identifier of the KMS key used to encrypt inference scheduler data by Amazon Lookout for Equipment.
LatestInferenceResult (string) --
Indicates whether the latest execution for the inference scheduler was Anomalous (anomalous events found) or Normal (no anomalous events found).
{'LabelsInputConfiguration': {'LabelGroupName': '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'
},
'LabelGroupName': '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.
LabelGroupName (string) --
The name of the label group to be used for 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.
{'InferenceSchedulerSummaries': {'LatestInferenceResult': 'ANOMALOUS | NORMAL'}}
Retrieves a list of all inference schedulers currently available for your account.
See also: AWS API Documentation
Request Syntax
client.list_inference_schedulers(
NextToken='string',
MaxResults=123,
InferenceSchedulerNameBeginsWith='string',
ModelName='string'
)
string
An opaque pagination token indicating where to continue the listing of inference schedulers.
integer
Specifies the maximum number of inference schedulers to list.
string
The beginning of the name of the inference schedulers to be listed.
string
The name of the ML model used by the inference scheduler to be listed.
dict
Response Syntax
{
'NextToken': 'string',
'InferenceSchedulerSummaries': [
{
'ModelName': 'string',
'ModelArn': 'string',
'InferenceSchedulerName': 'string',
'InferenceSchedulerArn': 'string',
'Status': 'PENDING'|'RUNNING'|'STOPPING'|'STOPPED',
'DataDelayOffsetInMinutes': 123,
'DataUploadFrequency': 'PT5M'|'PT10M'|'PT15M'|'PT30M'|'PT1H',
'LatestInferenceResult': 'ANOMALOUS'|'NORMAL'
},
]
}
Response Structure
(dict) --
NextToken (string) --
An opaque pagination token indicating where to continue the listing of inference schedulers.
InferenceSchedulerSummaries (list) --
Provides information about the specified inference scheduler, including data upload frequency, model name and ARN, and status.
(dict) --
Contains information about the specific inference scheduler, including data delay offset, model name and ARN, status, and so on.
ModelName (string) --
The name of the ML model used for the inference scheduler.
ModelArn (string) --
The Amazon Resource Name (ARN) of the ML model used by the inference scheduler.
InferenceSchedulerName (string) --
The name of the inference scheduler.
InferenceSchedulerArn (string) --
The Amazon Resource Name (ARN) of the inference scheduler.
Status (string) --
Indicates the status of the inference scheduler.
DataDelayOffsetInMinutes (integer) --
A period of time (in minutes) by which inference on the data is delayed after the data starts. For instance, if an offset delay time of five minutes was selected, inference will not begin on the data until the first data measurement after the five minute mark. For example, if five minutes is selected, the inference scheduler will wake up at the configured frequency with the additional five minute delay time to check the customer S3 bucket. The customer can upload data at the same frequency and they don't need to stop and restart the scheduler when uploading new data.
DataUploadFrequency (string) --
How often data is uploaded to the source S3 bucket for the input data. This value is the length of time between data uploads. For instance, if you select 5 minutes, Amazon Lookout for Equipment will upload the real-time data to the source bucket once every 5 minutes. This frequency also determines how often Amazon Lookout for Equipment starts a scheduled inference on your data. In this example, it starts once every 5 minutes.
LatestInferenceResult (string) --
Indicates whether the latest execution for the inference scheduler was Anomalous (anomalous events found) or Normal (no anomalous events found).