2023/07/31 - Amazon Lookout for Equipment - 8 new 9 updated api methods
Changes This release includes new import resource, model versioning and resource policy features.
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.
See also: AWS API Documentation
Request Syntax
client.list_model_versions(
ModelName='string',
NextToken='string',
MaxResults=123,
Status='IN_PROGRESS'|'SUCCESS'|'FAILED'|'IMPORT_IN_PROGRESS'|'CANCELED',
SourceType='TRAINING'|'RETRAINING'|'IMPORT',
CreatedAtEndTime=datetime(2015, 1, 1),
CreatedAtStartTime=datetime(2015, 1, 1),
MaxModelVersion=123,
MinModelVersion=123
)
string
[REQUIRED]
Then name of the machine learning model for which the model versions are to be listed.
string
If the total number of results exceeds the limit that the response can display, the response returns an opaque pagination token indicating where to continue the listing of machine learning model versions. Use this token in the NextToken field in the request to list the next page of results.
integer
Specifies the maximum number of machine learning model versions to list.
string
Filter the results based on the current status of the model version.
string
Filter the results based on the way the model version was generated.
datetime
Filter results to return all the model versions created before this time.
datetime
Filter results to return all the model versions created after this time.
integer
Specifies the highest version of the model to return in the list.
integer
Specifies the lowest version of the model to return in the list.
dict
Response Syntax
{
'NextToken': 'string',
'ModelVersionSummaries': [
{
'ModelName': 'string',
'ModelArn': 'string',
'ModelVersion': 123,
'ModelVersionArn': 'string',
'CreatedAt': datetime(2015, 1, 1),
'Status': 'IN_PROGRESS'|'SUCCESS'|'FAILED'|'IMPORT_IN_PROGRESS'|'CANCELED',
'SourceType': 'TRAINING'|'RETRAINING'|'IMPORT'
},
]
}
Response Structure
(dict) --
NextToken (string) --
If the total number of results exceeds the limit that the response can display, the response returns an opaque pagination token indicating where to continue the listing of machine learning model versions. Use this token in the NextToken field in the request to list the next page of results.
ModelVersionSummaries (list) --
Provides information on the specified model version, including the created time, model and dataset ARNs, and status.
(dict) --
Contains information about the specific model version.
ModelName (string) --
The name of the model that this model version is a version of.
ModelArn (string) --
The Amazon Resource Name (ARN) of the model that this model version is a version of.
ModelVersion (integer) --
The version of the model.
ModelVersionArn (string) --
The Amazon Resource Name (ARN) of the model version.
CreatedAt (datetime) --
The time when this model version was created.
Status (string) --
The current status of the model version.
SourceType (string) --
Indicates how this model version was generated.
Provides the details of a resource policy attached to a resource.
See also: AWS API Documentation
Request Syntax
client.describe_resource_policy(
ResourceArn='string'
)
string
[REQUIRED]
The Amazon Resource Name (ARN) of the resource that is associated with the resource policy.
dict
Response Syntax
{
'PolicyRevisionId': 'string',
'ResourcePolicy': 'string',
'CreationTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1)
}
Response Structure
(dict) --
PolicyRevisionId (string) --
A unique identifier for a revision of the resource policy.
ResourcePolicy (string) --
The resource policy in a JSON-formatted string.
CreationTime (datetime) --
The time when the resource policy was created.
LastModifiedTime (datetime) --
The time when the resource policy was last modified.
Creates a resource control policy for a given resource.
See also: AWS API Documentation
Request Syntax
client.put_resource_policy(
ResourceArn='string',
ResourcePolicy='string',
PolicyRevisionId='string',
ClientToken='string'
)
string
[REQUIRED]
The Amazon Resource Name (ARN) of the resource for which the policy is being created.
string
[REQUIRED]
The JSON-formatted resource policy to create.
string
A unique identifier for a revision of the resource policy.
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.
dict
Response Syntax
{
'ResourceArn': 'string',
'PolicyRevisionId': 'string'
}
Response Structure
(dict) --
ResourceArn (string) --
The Amazon Resource Name (ARN) of the resource for which the policy was created.
PolicyRevisionId (string) --
A unique identifier for a revision of the resource policy.
Imports a dataset.
See also: AWS API Documentation
Request Syntax
client.import_dataset(
SourceDatasetArn='string',
DatasetName='string',
ClientToken='string',
ServerSideKmsKeyId='string',
Tags=[
{
'Key': 'string',
'Value': 'string'
},
]
)
string
[REQUIRED]
The Amazon Resource Name (ARN) of the dataset to import.
string
The name of the machine learning dataset to be created. If the dataset already exists, Amazon Lookout for Equipment overwrites the existing dataset. If you don't specify this field, it is filled with the name of the source dataset.
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.
string
Provides the identifier of the KMS key key used to encrypt model data by Amazon Lookout for Equipment.
list
Any tags associated with the dataset to be 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.
dict
Response Syntax
{
'DatasetName': 'string',
'DatasetArn': 'string',
'Status': 'CREATED'|'INGESTION_IN_PROGRESS'|'ACTIVE'|'IMPORT_IN_PROGRESS',
'JobId': 'string'
}
Response Structure
(dict) --
DatasetName (string) --
The name of the created machine learning dataset.
DatasetArn (string) --
The Amazon Resource Name (ARN) of the dataset that was imported.
Status (string) --
The status of the ImportDataset operation.
JobId (string) --
A unique identifier for the job of importing the dataset.
Deletes the resource policy attached to the resource.
See also: AWS API Documentation
Request Syntax
client.delete_resource_policy(
ResourceArn='string'
)
string
[REQUIRED]
The Amazon Resource Name (ARN) of the resource for which the resource policy should be deleted.
None
Retrieves information about a specific machine learning model version.
See also: AWS API Documentation
Request Syntax
client.describe_model_version(
ModelName='string',
ModelVersion=123
)
string
[REQUIRED]
The name of the machine learning model that this version belongs to.
integer
[REQUIRED]
The version of the machine learning model.
dict
Response Syntax
{
'ModelName': 'string',
'ModelArn': 'string',
'ModelVersion': 123,
'ModelVersionArn': 'string',
'Status': 'IN_PROGRESS'|'SUCCESS'|'FAILED'|'IMPORT_IN_PROGRESS'|'CANCELED',
'SourceType': 'TRAINING'|'RETRAINING'|'IMPORT',
'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'
},
'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',
'SourceModelVersionArn': 'string',
'ImportJobStartTime': datetime(2015, 1, 1),
'ImportJobEndTime': datetime(2015, 1, 1),
'ImportedDataSizeInBytes': 123
}
Response Structure
(dict) --
ModelName (string) --
The name of the machine learning model that this version belongs to.
ModelArn (string) --
The Amazon Resource Name (ARN) of the parent machine learning model that this version belong to.
ModelVersion (integer) --
The version of the machine learning model.
ModelVersionArn (string) --
The Amazon Resource Name (ARN) of the model version.
Status (string) --
The current status of the model version.
SourceType (string) --
Indicates whether this model version was created by training or by importing.
DatasetName (string) --
The name of the dataset used to train the model version.
DatasetArn (string) --
The Amazon Resource Name (ARN) of the dataset used to train the model version.
Schema (string) --
The schema of the data used to train the model version.
LabelsInputConfiguration (dict) --
Contains the configuration information for the S3 location being used to hold label data.
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) --
The date on which the training data began being gathered. If you imported the version, this is the date that the training data in the source version began being gathered.
TrainingDataEndTime (datetime) --
The date on which the training data finished being gathered. If you imported the version, this is the date that the training data in the source version finished being gathered.
EvaluationDataStartTime (datetime) --
The date on which the data in the evaluation set began being gathered. If you imported the version, this is the date that the evaluation set data in the source version began being gathered.
EvaluationDataEndTime (datetime) --
The date on which the data in the evaluation set began being gathered. If you imported the version, this is the date that the evaluation set data in the source version finished being gathered.
RoleArn (string) --
The Amazon Resource Name (ARN) of the role that was used to train the model version.
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
TrainingExecutionStartTime (datetime) --
The time when the training of the version began.
TrainingExecutionEndTime (datetime) --
The time when the training of the version completed.
FailedReason (string) --
The failure message if the training of the model version failed.
ModelMetrics (string) --
Shows an aggregated summary, in JSON format, of the model's performance within the evaluation time range. These metrics are created when evaluating the model.
LastUpdatedTime (datetime) --
Indicates the last time the machine learning model version was updated.
CreatedAt (datetime) --
Indicates the time and date at which the machine learning model version was created.
ServerSideKmsKeyId (string) --
The identifier of the KMS key key used to encrypt model version 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.
SourceModelVersionArn (string) --
If model version was imported, then this field is the arn of the source model version.
ImportJobStartTime (datetime) --
The date and time when the import job began. This field appears if the model version was imported.
ImportJobEndTime (datetime) --
The date and time when the import job completed. This field appears if the model version was imported.
ImportedDataSizeInBytes (integer) --
The size in bytes of the imported data. This field appears if the model version was imported.
Sets the active model version for a given machine learning model.
See also: AWS API Documentation
Request Syntax
client.update_active_model_version(
ModelName='string',
ModelVersion=123
)
string
[REQUIRED]
The name of the machine learning model for which the active model version is being set.
integer
[REQUIRED]
The version of the machine learning model for which the active model version is being set.
dict
Response Syntax
{
'ModelName': 'string',
'ModelArn': 'string',
'CurrentActiveVersion': 123,
'PreviousActiveVersion': 123,
'CurrentActiveVersionArn': 'string',
'PreviousActiveVersionArn': 'string'
}
Response Structure
(dict) --
ModelName (string) --
The name of the machine learning model for which the active model version was set.
ModelArn (string) --
The Amazon Resource Name (ARN) of the machine learning model for which the active model version was set.
CurrentActiveVersion (integer) --
The version that is currently active of the machine learning model for which the active model version was set.
PreviousActiveVersion (integer) --
The previous version that was active of the machine learning model for which the active model version was set.
CurrentActiveVersionArn (string) --
The Amazon Resource Name (ARN) of the machine learning model version that is the current active model version.
PreviousActiveVersionArn (string) --
The Amazon Resource Name (ARN) of the machine learning model version that was the previous active model version.
Imports a model that has been trained successfully.
See also: AWS API Documentation
Request Syntax
client.import_model_version(
SourceModelVersionArn='string',
ModelName='string',
DatasetName='string',
LabelsInputConfiguration={
'S3InputConfiguration': {
'Bucket': 'string',
'Prefix': 'string'
},
'LabelGroupName': 'string'
},
ClientToken='string',
RoleArn='string',
ServerSideKmsKeyId='string',
Tags=[
{
'Key': 'string',
'Value': 'string'
},
]
)
string
[REQUIRED]
The Amazon Resource Name (ARN) of the model version to import.
string
The name for the machine learning model to be created. If the model already exists, Amazon Lookout for Equipment creates a new version. If you do not specify this field, it is filled with the name of the source model.
string
[REQUIRED]
The name of the dataset for the machine learning model being imported.
dict
Contains the configuration information for the S3 location being used to hold label data.
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.
string
The Amazon Resource Name (ARN) of a role with permission to access the data source being used to create the machine learning model.
string
Provides the identifier of the KMS key key used to encrypt model data by Amazon Lookout for Equipment.
list
The tags associated with the machine learning model to be 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.
dict
Response Syntax
{
'ModelName': 'string',
'ModelArn': 'string',
'ModelVersionArn': 'string',
'ModelVersion': 123,
'Status': 'IN_PROGRESS'|'SUCCESS'|'FAILED'|'IMPORT_IN_PROGRESS'|'CANCELED'
}
Response Structure
(dict) --
ModelName (string) --
The name for the machine learning model.
ModelArn (string) --
The Amazon Resource Name (ARN) of the model being created.
ModelVersionArn (string) --
The Amazon Resource Name (ARN) of the model version being created.
ModelVersion (integer) --
The version of the model being created.
Status (string) --
The status of the ImportModelVersion operation.
{'Status': {'IMPORT_IN_PROGRESS'}}
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.
See also: AWS API Documentation
Request Syntax
client.create_dataset(
DatasetName='string',
DatasetSchema={
'InlineDataSchema': 'string'
},
ServerSideKmsKeyId='string',
ClientToken='string',
Tags=[
{
'Key': 'string',
'Value': 'string'
},
]
)
string
[REQUIRED]
The name of the dataset being created.
dict
A JSON description of the data that is in each time series dataset, including names, column names, and data types.
InlineDataSchema (string) --
string
Provides the identifier of the KMS key used to encrypt dataset data by Amazon Lookout for Equipment.
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.
list
Any tags associated with the ingested data described in the dataset.
(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
{
'DatasetName': 'string',
'DatasetArn': 'string',
'Status': 'CREATED'|'INGESTION_IN_PROGRESS'|'ACTIVE'|'IMPORT_IN_PROGRESS'
}
Response Structure
(dict) --
DatasetName (string) --
The name of the dataset being created.
DatasetArn (string) --
The Amazon Resource Name (ARN) of the dataset being created.
Status (string) --
Indicates the status of the CreateDataset operation.
{'Status': {'IMPORT_IN_PROGRESS'}}
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'|'IMPORT_IN_PROGRESS'
}
Response Structure
(dict) --
ModelArn (string) --
The Amazon Resource Name (ARN) of the model being created.
Status (string) --
Indicates the status of the CreateModel operation.
{'SourceDatasetArn': 'string', 'Status': {'IMPORT_IN_PROGRESS'}}
Provides information on a specific data ingestion job such as creation time, dataset ARN, and status.
See also: AWS API Documentation
Request Syntax
client.describe_data_ingestion_job(
JobId='string'
)
string
[REQUIRED]
The job ID of the data ingestion job.
dict
Response Syntax
{
'JobId': 'string',
'DatasetArn': 'string',
'IngestionInputConfiguration': {
'S3InputConfiguration': {
'Bucket': 'string',
'Prefix': 'string',
'KeyPattern': 'string'
}
},
'RoleArn': 'string',
'CreatedAt': datetime(2015, 1, 1),
'Status': 'IN_PROGRESS'|'SUCCESS'|'FAILED'|'IMPORT_IN_PROGRESS',
'FailedReason': 'string',
'DataQualitySummary': {
'InsufficientSensorData': {
'MissingCompleteSensorData': {
'AffectedSensorCount': 123
},
'SensorsWithShortDateRange': {
'AffectedSensorCount': 123
}
},
'MissingSensorData': {
'AffectedSensorCount': 123,
'TotalNumberOfMissingValues': 123
},
'InvalidSensorData': {
'AffectedSensorCount': 123,
'TotalNumberOfInvalidValues': 123
},
'UnsupportedTimestamps': {
'TotalNumberOfUnsupportedTimestamps': 123
},
'DuplicateTimestamps': {
'TotalNumberOfDuplicateTimestamps': 123
}
},
'IngestedFilesSummary': {
'TotalNumberOfFiles': 123,
'IngestedNumberOfFiles': 123,
'DiscardedFiles': [
{
'Bucket': 'string',
'Key': 'string'
},
]
},
'StatusDetail': 'string',
'IngestedDataSize': 123,
'DataStartTime': datetime(2015, 1, 1),
'DataEndTime': datetime(2015, 1, 1),
'SourceDatasetArn': 'string'
}
Response Structure
(dict) --
JobId (string) --
Indicates the job ID of the data ingestion job.
DatasetArn (string) --
The Amazon Resource Name (ARN) of the dataset being used in the data ingestion job.
IngestionInputConfiguration (dict) --
Specifies the S3 location configuration for the data input for the data ingestion job.
S3InputConfiguration (dict) --
The location information for the S3 bucket used for input data for the data ingestion.
Bucket (string) --
The name of the S3 bucket used for the input data for the data ingestion.
Prefix (string) --
The prefix for the S3 location being used for the input data for the data ingestion.
KeyPattern (string) --
The pattern for matching the Amazon S3 files that will be used for ingestion. If the schema was created previously without any KeyPattern, then the default KeyPattern {prefix}/{component_name}/* is used to download files from Amazon S3 according to the schema. This field is required when ingestion is being done for the first time.
Valid Values: {prefix}/{component_name}_* | {prefix}/{component_name}/* | {prefix}/{component_name}[DELIMITER]* (Allowed delimiters : space, dot, underscore, hyphen)
RoleArn (string) --
The Amazon Resource Name (ARN) of an IAM role with permission to access the data source being ingested.
CreatedAt (datetime) --
The time at which the data ingestion job was created.
Status (string) --
Indicates the status of the DataIngestionJob operation.
FailedReason (string) --
Specifies the reason for failure when a data ingestion job has failed.
DataQualitySummary (dict) --
Gives statistics about a completed ingestion job. These statistics primarily relate to quantifying incorrect data such as MissingCompleteSensorData, MissingSensorData, UnsupportedDateFormats, InsufficientSensorData, and DuplicateTimeStamps.
InsufficientSensorData (dict) --
Parameter that gives information about insufficient data for sensors in the dataset. This includes information about those sensors that have complete data missing and those with a short date range.
MissingCompleteSensorData (dict) --
Parameter that describes the total number of sensors that have data completely missing for it.
AffectedSensorCount (integer) --
Indicates the number of sensors that have data missing completely.
SensorsWithShortDateRange (dict) --
Parameter that describes the total number of sensors that have a short date range of less than 90 days of data overall.
AffectedSensorCount (integer) --
Indicates the number of sensors that have less than 90 days of data.
MissingSensorData (dict) --
Parameter that gives information about data that is missing over all the sensors in the input data.
AffectedSensorCount (integer) --
Indicates the number of sensors that have atleast some data missing.
TotalNumberOfMissingValues (integer) --
Indicates the total number of missing values across all the sensors.
InvalidSensorData (dict) --
Parameter that gives information about data that is invalid over all the sensors in the input data.
AffectedSensorCount (integer) --
Indicates the number of sensors that have at least some invalid values.
TotalNumberOfInvalidValues (integer) --
Indicates the total number of invalid values across all the sensors.
UnsupportedTimestamps (dict) --
Parameter that gives information about unsupported timestamps in the input data.
TotalNumberOfUnsupportedTimestamps (integer) --
Indicates the total number of unsupported timestamps across the ingested data.
DuplicateTimestamps (dict) --
Parameter that gives information about duplicate timestamps in the input data.
TotalNumberOfDuplicateTimestamps (integer) --
Indicates the total number of duplicate timestamps.
IngestedFilesSummary (dict) --
Gives statistics about how many files have been ingested, and which files have not been ingested, for a particular ingestion job.
TotalNumberOfFiles (integer) --
Indicates the total number of files that were submitted for ingestion.
IngestedNumberOfFiles (integer) --
Indicates the number of files that were successfully ingested.
DiscardedFiles (list) --
Indicates the number of files that were discarded. A file could be discarded because its format is invalid (for example, a jpg or pdf) or not readable.
(dict) --
Contains information about an S3 bucket.
Bucket (string) --
The name of the specific S3 bucket.
Key (string) --
The Amazon Web Services Key Management Service (KMS key) key being used to encrypt the S3 object. Without this key, data in the bucket is not accessible.
StatusDetail (string) --
Provides details about status of the ingestion job that is currently in progress.
IngestedDataSize (integer) --
Indicates the size of the ingested dataset.
DataStartTime (datetime) --
Indicates the earliest timestamp corresponding to data that was successfully ingested during this specific ingestion job.
DataEndTime (datetime) --
Indicates the latest timestamp corresponding to data that was successfully ingested during this specific ingestion job.
SourceDatasetArn (string) --
The Amazon Resource Name (ARN) of the source dataset from which the data used for the data ingestion job was imported from.
{'SourceDatasetArn': 'string', 'Status': {'IMPORT_IN_PROGRESS'}}
Provides a JSON description of the data in each time series dataset, including names, column names, and data types.
See also: AWS API Documentation
Request Syntax
client.describe_dataset(
DatasetName='string'
)
string
[REQUIRED]
The name of the dataset to be described.
dict
Response Syntax
{
'DatasetName': 'string',
'DatasetArn': 'string',
'CreatedAt': datetime(2015, 1, 1),
'LastUpdatedAt': datetime(2015, 1, 1),
'Status': 'CREATED'|'INGESTION_IN_PROGRESS'|'ACTIVE'|'IMPORT_IN_PROGRESS',
'Schema': 'string',
'ServerSideKmsKeyId': 'string',
'IngestionInputConfiguration': {
'S3InputConfiguration': {
'Bucket': 'string',
'Prefix': 'string',
'KeyPattern': 'string'
}
},
'DataQualitySummary': {
'InsufficientSensorData': {
'MissingCompleteSensorData': {
'AffectedSensorCount': 123
},
'SensorsWithShortDateRange': {
'AffectedSensorCount': 123
}
},
'MissingSensorData': {
'AffectedSensorCount': 123,
'TotalNumberOfMissingValues': 123
},
'InvalidSensorData': {
'AffectedSensorCount': 123,
'TotalNumberOfInvalidValues': 123
},
'UnsupportedTimestamps': {
'TotalNumberOfUnsupportedTimestamps': 123
},
'DuplicateTimestamps': {
'TotalNumberOfDuplicateTimestamps': 123
}
},
'IngestedFilesSummary': {
'TotalNumberOfFiles': 123,
'IngestedNumberOfFiles': 123,
'DiscardedFiles': [
{
'Bucket': 'string',
'Key': 'string'
},
]
},
'RoleArn': 'string',
'DataStartTime': datetime(2015, 1, 1),
'DataEndTime': datetime(2015, 1, 1),
'SourceDatasetArn': 'string'
}
Response Structure
(dict) --
DatasetName (string) --
The name of the dataset being described.
DatasetArn (string) --
The Amazon Resource Name (ARN) of the dataset being described.
CreatedAt (datetime) --
Specifies the time the dataset was created in Lookout for Equipment.
LastUpdatedAt (datetime) --
Specifies the time the dataset was last updated, if it was.
Status (string) --
Indicates the status of the dataset.
Schema (string) --
A JSON description of the data that is in each time series dataset, including names, column names, and data types.
ServerSideKmsKeyId (string) --
Provides the identifier of the KMS key used to encrypt dataset data by Amazon Lookout for Equipment.
IngestionInputConfiguration (dict) --
Specifies the S3 location configuration for the data input for the data ingestion job.
S3InputConfiguration (dict) --
The location information for the S3 bucket used for input data for the data ingestion.
Bucket (string) --
The name of the S3 bucket used for the input data for the data ingestion.
Prefix (string) --
The prefix for the S3 location being used for the input data for the data ingestion.
KeyPattern (string) --
The pattern for matching the Amazon S3 files that will be used for ingestion. If the schema was created previously without any KeyPattern, then the default KeyPattern {prefix}/{component_name}/* is used to download files from Amazon S3 according to the schema. This field is required when ingestion is being done for the first time.
Valid Values: {prefix}/{component_name}_* | {prefix}/{component_name}/* | {prefix}/{component_name}[DELIMITER]* (Allowed delimiters : space, dot, underscore, hyphen)
DataQualitySummary (dict) --
Gives statistics associated with the given dataset for the latest successful associated ingestion job id. These statistics primarily relate to quantifying incorrect data such as MissingCompleteSensorData, MissingSensorData, UnsupportedDateFormats, InsufficientSensorData, and DuplicateTimeStamps.
InsufficientSensorData (dict) --
Parameter that gives information about insufficient data for sensors in the dataset. This includes information about those sensors that have complete data missing and those with a short date range.
MissingCompleteSensorData (dict) --
Parameter that describes the total number of sensors that have data completely missing for it.
AffectedSensorCount (integer) --
Indicates the number of sensors that have data missing completely.
SensorsWithShortDateRange (dict) --
Parameter that describes the total number of sensors that have a short date range of less than 90 days of data overall.
AffectedSensorCount (integer) --
Indicates the number of sensors that have less than 90 days of data.
MissingSensorData (dict) --
Parameter that gives information about data that is missing over all the sensors in the input data.
AffectedSensorCount (integer) --
Indicates the number of sensors that have atleast some data missing.
TotalNumberOfMissingValues (integer) --
Indicates the total number of missing values across all the sensors.
InvalidSensorData (dict) --
Parameter that gives information about data that is invalid over all the sensors in the input data.
AffectedSensorCount (integer) --
Indicates the number of sensors that have at least some invalid values.
TotalNumberOfInvalidValues (integer) --
Indicates the total number of invalid values across all the sensors.
UnsupportedTimestamps (dict) --
Parameter that gives information about unsupported timestamps in the input data.
TotalNumberOfUnsupportedTimestamps (integer) --
Indicates the total number of unsupported timestamps across the ingested data.
DuplicateTimestamps (dict) --
Parameter that gives information about duplicate timestamps in the input data.
TotalNumberOfDuplicateTimestamps (integer) --
Indicates the total number of duplicate timestamps.
IngestedFilesSummary (dict) --
IngestedFilesSummary associated with the given dataset for the latest successful associated ingestion job id.
TotalNumberOfFiles (integer) --
Indicates the total number of files that were submitted for ingestion.
IngestedNumberOfFiles (integer) --
Indicates the number of files that were successfully ingested.
DiscardedFiles (list) --
Indicates the number of files that were discarded. A file could be discarded because its format is invalid (for example, a jpg or pdf) or not readable.
(dict) --
Contains information about an S3 bucket.
Bucket (string) --
The name of the specific S3 bucket.
Key (string) --
The Amazon Web Services Key Management Service (KMS key) key being used to encrypt the S3 object. Without this key, data in the bucket is not accessible.
RoleArn (string) --
The Amazon Resource Name (ARN) of the IAM role that you are using for this the data ingestion job.
DataStartTime (datetime) --
Indicates the earliest timestamp corresponding to data that was successfully ingested during the most recent ingestion of this particular dataset.
DataEndTime (datetime) --
Indicates the latest timestamp corresponding to data that was successfully ingested during the most recent ingestion of this particular dataset.
SourceDatasetArn (string) --
The Amazon Resource Name (ARN) of the source dataset from which the current data being described was imported from.
{'ActiveModelVersion': 'long',
'ActiveModelVersionArn': 'string',
'ImportJobEndTime': 'timestamp',
'ImportJobStartTime': 'timestamp',
'ModelVersionActivatedAt': 'timestamp',
'PreviousActiveModelVersion': 'long',
'PreviousActiveModelVersionArn': 'string',
'PreviousModelVersionActivatedAt': 'timestamp',
'SourceModelVersionArn': 'string',
'Status': {'IMPORT_IN_PROGRESS'}}
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'|'IMPORT_IN_PROGRESS',
'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',
'SourceModelVersionArn': 'string',
'ImportJobStartTime': datetime(2015, 1, 1),
'ImportJobEndTime': datetime(2015, 1, 1),
'ActiveModelVersion': 123,
'ActiveModelVersionArn': 'string',
'ModelVersionActivatedAt': datetime(2015, 1, 1),
'PreviousActiveModelVersion': 123,
'PreviousActiveModelVersionArn': 'string',
'PreviousModelVersionActivatedAt': datetime(2015, 1, 1)
}
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.
SourceModelVersionArn (string) --
The Amazon Resource Name (ARN) of the source model version. This field appears if the active model version was imported.
ImportJobStartTime (datetime) --
The date and time when the import job was started. This field appears if the active model version was imported.
ImportJobEndTime (datetime) --
The date and time when the import job was completed. This field appears if the active model version was imported.
ActiveModelVersion (integer) --
The name of the model version used by the inference schedular when running a scheduled inference execution.
ActiveModelVersionArn (string) --
The Amazon Resource Name (ARN) of the model version used by the inference scheduler when running a scheduled inference execution.
ModelVersionActivatedAt (datetime) --
The date the active model version was activated.
PreviousActiveModelVersion (integer) --
The model version that was set as the active model version prior to the current active model version.
PreviousActiveModelVersionArn (string) --
The ARN of the model version that was set as the active model version prior to the current active model version.
PreviousModelVersionActivatedAt (datetime) --
The date and time when the previous active model version was activated.
{'Status': {'IMPORT_IN_PROGRESS'}}
Response {'DataIngestionJobSummaries': {'Status': {'IMPORT_IN_PROGRESS'}}}
Provides a list of all data ingestion jobs, including dataset name and ARN, S3 location of the input data, status, and so on.
See also: AWS API Documentation
Request Syntax
client.list_data_ingestion_jobs(
DatasetName='string',
NextToken='string',
MaxResults=123,
Status='IN_PROGRESS'|'SUCCESS'|'FAILED'|'IMPORT_IN_PROGRESS'
)
string
The name of the dataset being used for the data ingestion job.
string
An opaque pagination token indicating where to continue the listing of data ingestion jobs.
integer
Specifies the maximum number of data ingestion jobs to list.
string
Indicates the status of the data ingestion job.
dict
Response Syntax
{
'NextToken': 'string',
'DataIngestionJobSummaries': [
{
'JobId': 'string',
'DatasetName': 'string',
'DatasetArn': 'string',
'IngestionInputConfiguration': {
'S3InputConfiguration': {
'Bucket': 'string',
'Prefix': 'string',
'KeyPattern': 'string'
}
},
'Status': 'IN_PROGRESS'|'SUCCESS'|'FAILED'|'IMPORT_IN_PROGRESS'
},
]
}
Response Structure
(dict) --
NextToken (string) --
An opaque pagination token indicating where to continue the listing of data ingestion jobs.
DataIngestionJobSummaries (list) --
Specifies information about the specific data ingestion job, including dataset name and status.
(dict) --
Provides information about a specified data ingestion job, including dataset information, data ingestion configuration, and status.
JobId (string) --
Indicates the job ID of the data ingestion job.
DatasetName (string) --
The name of the dataset used for the data ingestion job.
DatasetArn (string) --
The Amazon Resource Name (ARN) of the dataset used in the data ingestion job.
IngestionInputConfiguration (dict) --
Specifies information for the input data for the data inference job, including data Amazon S3 location parameters.
S3InputConfiguration (dict) --
The location information for the S3 bucket used for input data for the data ingestion.
Bucket (string) --
The name of the S3 bucket used for the input data for the data ingestion.
Prefix (string) --
The prefix for the S3 location being used for the input data for the data ingestion.
KeyPattern (string) --
The pattern for matching the Amazon S3 files that will be used for ingestion. If the schema was created previously without any KeyPattern, then the default KeyPattern {prefix}/{component_name}/* is used to download files from Amazon S3 according to the schema. This field is required when ingestion is being done for the first time.
Valid Values: {prefix}/{component_name}_* | {prefix}/{component_name}/* | {prefix}/{component_name}[DELIMITER]* (Allowed delimiters : space, dot, underscore, hyphen)
Status (string) --
Indicates the status of the data ingestion job.
{'DatasetSummaries': {'Status': {'IMPORT_IN_PROGRESS'}}}
Lists all datasets currently available in your account, filtering on the dataset name.
See also: AWS API Documentation
Request Syntax
client.list_datasets(
NextToken='string',
MaxResults=123,
DatasetNameBeginsWith='string'
)
string
An opaque pagination token indicating where to continue the listing of datasets.
integer
Specifies the maximum number of datasets to list.
string
The beginning of the name of the datasets to be listed.
dict
Response Syntax
{
'NextToken': 'string',
'DatasetSummaries': [
{
'DatasetName': 'string',
'DatasetArn': 'string',
'Status': 'CREATED'|'INGESTION_IN_PROGRESS'|'ACTIVE'|'IMPORT_IN_PROGRESS',
'CreatedAt': datetime(2015, 1, 1)
},
]
}
Response Structure
(dict) --
NextToken (string) --
An opaque pagination token indicating where to continue the listing of datasets.
DatasetSummaries (list) --
Provides information about the specified dataset, including creation time, dataset ARN, and status.
(dict) --
Contains information about the specific data set, including name, ARN, and status.
DatasetName (string) --
The name of the dataset.
DatasetArn (string) --
The Amazon Resource Name (ARN) of the specified dataset.
Status (string) --
Indicates the status of the dataset.
CreatedAt (datetime) --
The time at which the dataset was created in Amazon Lookout for Equipment.
{'Status': {'IMPORT_IN_PROGRESS'}}
Response {'ModelSummaries': {'ActiveModelVersion': 'long',
'ActiveModelVersionArn': 'string',
'Status': {'IMPORT_IN_PROGRESS'}}}
Generates a list of all models in the account, including model name and ARN, dataset, and status.
See also: AWS API Documentation
Request Syntax
client.list_models(
NextToken='string',
MaxResults=123,
Status='IN_PROGRESS'|'SUCCESS'|'FAILED'|'IMPORT_IN_PROGRESS',
ModelNameBeginsWith='string',
DatasetNameBeginsWith='string'
)
string
An opaque pagination token indicating where to continue the listing of ML models.
integer
Specifies the maximum number of ML models to list.
string
The status of the ML model.
string
The beginning of the name of the ML models being listed.
string
The beginning of the name of the dataset of the ML models to be listed.
dict
Response Syntax
{
'NextToken': 'string',
'ModelSummaries': [
{
'ModelName': 'string',
'ModelArn': 'string',
'DatasetName': 'string',
'DatasetArn': 'string',
'Status': 'IN_PROGRESS'|'SUCCESS'|'FAILED'|'IMPORT_IN_PROGRESS',
'CreatedAt': datetime(2015, 1, 1),
'ActiveModelVersion': 123,
'ActiveModelVersionArn': 'string'
},
]
}
Response Structure
(dict) --
NextToken (string) --
An opaque pagination token indicating where to continue the listing of ML models.
ModelSummaries (list) --
Provides information on the specified model, including created time, model and dataset ARNs, and status.
(dict) --
Provides information about the specified ML model, including dataset and model names and ARNs, as well as status.
ModelName (string) --
The name of the ML model.
ModelArn (string) --
The Amazon Resource Name (ARN) of the ML model.
DatasetName (string) --
The name of the dataset being used for the ML model.
DatasetArn (string) --
The Amazon Resource Name (ARN) of the dataset used to create the model.
Status (string) --
Indicates the status of the ML model.
CreatedAt (datetime) --
The time at which the specific model was created.
ActiveModelVersion (integer) --
The model version that the inference scheduler uses to run an inference execution.
ActiveModelVersionArn (string) --
The Amazon Resource Name (ARN) of the model version that is set as active. The active model version is the model version that the inference scheduler uses to run an inference execution.
{'Status': {'IMPORT_IN_PROGRESS'}}
Starts a data ingestion job. Amazon Lookout for Equipment returns the job status.
See also: AWS API Documentation
Request Syntax
client.start_data_ingestion_job(
DatasetName='string',
IngestionInputConfiguration={
'S3InputConfiguration': {
'Bucket': 'string',
'Prefix': 'string',
'KeyPattern': 'string'
}
},
RoleArn='string',
ClientToken='string'
)
string
[REQUIRED]
The name of the dataset being used by the data ingestion job.
dict
[REQUIRED]
Specifies information for the input data for the data ingestion job, including dataset S3 location.
S3InputConfiguration (dict) -- [REQUIRED]
The location information for the S3 bucket used for input data for the data ingestion.
Bucket (string) -- [REQUIRED]
The name of the S3 bucket used for the input data for the data ingestion.
Prefix (string) --
The prefix for the S3 location being used for the input data for the data ingestion.
KeyPattern (string) --
The pattern for matching the Amazon S3 files that will be used for ingestion. If the schema was created previously without any KeyPattern, then the default KeyPattern {prefix}/{component_name}/* is used to download files from Amazon S3 according to the schema. This field is required when ingestion is being done for the first time.
Valid Values: {prefix}/{component_name}_* | {prefix}/{component_name}/* | {prefix}/{component_name}[DELIMITER]* (Allowed delimiters : space, dot, underscore, hyphen)
string
[REQUIRED]
The Amazon Resource Name (ARN) of a role with permission to access the data source for the data ingestion job.
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.
dict
Response Syntax
{
'JobId': 'string',
'Status': 'IN_PROGRESS'|'SUCCESS'|'FAILED'|'IMPORT_IN_PROGRESS'
}
Response Structure
(dict) --
JobId (string) --
Indicates the job ID of the data ingestion job.
Status (string) --
Indicates the status of the StartDataIngestionJob operation.