/AWS1/CL_LOEMODELSUMMARY

Provides information about the specified machine learning model, including dataset and model names and ARNs, as well as status.

CONSTRUCTOR

IMPORTING

Optional arguments:

iv_modelname TYPE /AWS1/LOEMODELNAME /AWS1/LOEMODELNAME

The name of the machine learning model.

iv_modelarn TYPE /AWS1/LOEMODELARN /AWS1/LOEMODELARN

The Amazon Resource Name (ARN) of the machine learning model.

iv_datasetname TYPE /AWS1/LOEDATASETNAME /AWS1/LOEDATASETNAME

The name of the dataset being used for the machine learning model.

iv_datasetarn TYPE /AWS1/LOEDATASETARN /AWS1/LOEDATASETARN

The Amazon Resource Name (ARN) of the dataset used to create the model.

iv_status TYPE /AWS1/LOEMODELSTATUS /AWS1/LOEMODELSTATUS

Indicates the status of the machine learning model.

iv_createdat TYPE /AWS1/LOETIMESTAMP /AWS1/LOETIMESTAMP

The time at which the specific model was created.

iv_activemodelversion TYPE /AWS1/LOEMODELVERSION /AWS1/LOEMODELVERSION

The model version that the inference scheduler uses to run an inference execution.

iv_activemodelversionarn TYPE /AWS1/LOEMODELVERSIONARN /AWS1/LOEMODELVERSIONARN

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.

iv_latestschddretrnstatus TYPE /AWS1/LOEMODELVERSIONSTATUS /AWS1/LOEMODELVERSIONSTATUS

Indicates the status of the most recent scheduled retraining run.

iv_latestschddretrnmodelvrs TYPE /AWS1/LOEMODELVERSION /AWS1/LOEMODELVERSION

Indicates the most recent model version that was generated by retraining.

iv_latestschddretrnstarttime TYPE /AWS1/LOETIMESTAMP /AWS1/LOETIMESTAMP

Indicates the start time of the most recent scheduled retraining run.

iv_nextschddretrnstartdate TYPE /AWS1/LOETIMESTAMP /AWS1/LOETIMESTAMP

Indicates the date that the next scheduled retraining run will start on. Lookout for Equipment truncates the time you provide to the nearest UTC day.

iv_retrainingschedulerstatus TYPE /AWS1/LOERETRNSCHEDULERSTATUS /AWS1/LOERETRNSCHEDULERSTATUS

Indicates the status of the retraining scheduler.

io_modeldiagnosticsoutconf TYPE REF TO /AWS1/CL_LOEMDELDIAGNOSTICSO00 /AWS1/CL_LOEMDELDIAGNOSTICSO00

ModelDiagnosticsOutputConfiguration

iv_modelquality TYPE /AWS1/LOEMODELQUALITY /AWS1/LOEMODELQUALITY

Provides a quality assessment for a model that uses labels. If Lookout for Equipment determines that the model quality is poor based on training metrics, the value is POOR_QUALITY_DETECTED. Otherwise, the value is QUALITY_THRESHOLD_MET.

If the model is unlabeled, the model quality can't be assessed and the value of ModelQuality is CANNOT_DETERMINE_QUALITY. In this situation, you can get a model quality assessment by adding labels to the input dataset and retraining the model.

For information about using labels with your models, see Understanding labeling.

For information about improving the quality of a model, see Best practices with Amazon Lookout for Equipment.

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Queryable Attributes

ModelName

The name of the machine learning model.

Accessible with the following methods

Method	Description
GET_MODELNAME()	Getter for MODELNAME, with configurable default
ASK_MODELNAME()	Getter for MODELNAME w/ exceptions if field has no value
HAS_MODELNAME()	Determine if MODELNAME has a value

ModelArn

The Amazon Resource Name (ARN) of the machine learning model.

Accessible with the following methods

Method	Description
GET_MODELARN()	Getter for MODELARN, with configurable default
ASK_MODELARN()	Getter for MODELARN w/ exceptions if field has no value
HAS_MODELARN()	Determine if MODELARN has a value

DatasetName

The name of the dataset being used for the machine learning model.

Accessible with the following methods

Method	Description
GET_DATASETNAME()	Getter for DATASETNAME, with configurable default
ASK_DATASETNAME()	Getter for DATASETNAME w/ exceptions if field has no value
HAS_DATASETNAME()	Determine if DATASETNAME has a value

DatasetArn

The Amazon Resource Name (ARN) of the dataset used to create the model.

Accessible with the following methods

Method	Description
GET_DATASETARN()	Getter for DATASETARN, with configurable default
ASK_DATASETARN()	Getter for DATASETARN w/ exceptions if field has no value
HAS_DATASETARN()	Determine if DATASETARN has a value

Status

Indicates the status of the machine learning model.

Accessible with the following methods

Method	Description
GET_STATUS()	Getter for STATUS, with configurable default
ASK_STATUS()	Getter for STATUS w/ exceptions if field has no value
HAS_STATUS()	Determine if STATUS has a value

CreatedAt

The time at which the specific model was created.

Accessible with the following methods

Method	Description
GET_CREATEDAT()	Getter for CREATEDAT, with configurable default
ASK_CREATEDAT()	Getter for CREATEDAT w/ exceptions if field has no value
HAS_CREATEDAT()	Determine if CREATEDAT has a value

ActiveModelVersion

The model version that the inference scheduler uses to run an inference execution.

Accessible with the following methods

Method	Description
GET_ACTIVEMODELVERSION()	Getter for ACTIVEMODELVERSION, with configurable default
ASK_ACTIVEMODELVERSION()	Getter for ACTIVEMODELVERSION w/ exceptions if field has no
HAS_ACTIVEMODELVERSION()	Determine if ACTIVEMODELVERSION has a value

ActiveModelVersionArn

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.

Accessible with the following methods

Method	Description
GET_ACTIVEMODELVERSIONARN()	Getter for ACTIVEMODELVERSIONARN, with configurable default
ASK_ACTIVEMODELVERSIONARN()	Getter for ACTIVEMODELVERSIONARN w/ exceptions if field has
HAS_ACTIVEMODELVERSIONARN()	Determine if ACTIVEMODELVERSIONARN has a value

LatestScheduledRetrainingStatus

Indicates the status of the most recent scheduled retraining run.

Accessible with the following methods

Method	Description
GET_LATESTSCHDDRETRNSTATUS()	Getter for LATESTSCHEDULEDRETRNSTATUS, with configurable def
ASK_LATESTSCHDDRETRNSTATUS()	Getter for LATESTSCHEDULEDRETRNSTATUS w/ exceptions if field
HAS_LATESTSCHDDRETRNSTATUS()	Determine if LATESTSCHEDULEDRETRNSTATUS has a value

LatestScheduledRetrainingModelVersion

Indicates the most recent model version that was generated by retraining.

Accessible with the following methods

Method	Description
GET_LATESTSCHDDRETRNMODELVRS()	Getter for LATESTSCHDDRETRNMODELVERSION, with configurable d
ASK_LATESTSCHDDRETRNMODELVRS()	Getter for LATESTSCHDDRETRNMODELVERSION w/ exceptions if fie
HAS_LATESTSCHDDRETRNMODELVRS()	Determine if LATESTSCHDDRETRNMODELVERSION has a value

LatestScheduledRetrainingStartTime

Indicates the start time of the most recent scheduled retraining run.

Accessible with the following methods

Method	Description
GET_LATESTSCHDDRETRNSTRTTIME()	Getter for LATESTSCHDDRETRNSTARTTIME, with configurable defa
ASK_LATESTSCHDDRETRNSTRTTIME()	Getter for LATESTSCHDDRETRNSTARTTIME w/ exceptions if field
HAS_LATESTSCHDDRETRNSTRTTIME()	Determine if LATESTSCHDDRETRNSTARTTIME has a value

NextScheduledRetrainingStartDate

Indicates the date that the next scheduled retraining run will start on. Lookout for Equipment truncates the time you provide to the nearest UTC day.

Accessible with the following methods

Method	Description
GET_NEXTSCHDDRETRNSTARTDATE()	Getter for NEXTSCHEDULEDRETRNSTARTDATE, with configurable de
ASK_NEXTSCHDDRETRNSTARTDATE()	Getter for NEXTSCHEDULEDRETRNSTARTDATE w/ exceptions if fiel
HAS_NEXTSCHDDRETRNSTARTDATE()	Determine if NEXTSCHEDULEDRETRNSTARTDATE has a value

RetrainingSchedulerStatus

Indicates the status of the retraining scheduler.

Accessible with the following methods

Method	Description
GET_RETRNSCHEDULERSTATUS()	Getter for RETRAININGSCHEDULERSTATUS, with configurable defa
ASK_RETRNSCHEDULERSTATUS()	Getter for RETRAININGSCHEDULERSTATUS w/ exceptions if field
HAS_RETRNSCHEDULERSTATUS()	Determine if RETRAININGSCHEDULERSTATUS has a value

ModelDiagnosticsOutputConfiguration

ModelDiagnosticsOutputConfiguration

Accessible with the following methods

Method	Description
GET_MODELDIAGNOSTICSOUTCONF()	Getter for MODELDIAGNOSTICSOUTPUTCONF

ModelQuality

Provides a quality assessment for a model that uses labels. If Lookout for Equipment determines that the model quality is poor based on training metrics, the value is POOR_QUALITY_DETECTED. Otherwise, the value is QUALITY_THRESHOLD_MET.

If the model is unlabeled, the model quality can't be assessed and the value of ModelQuality is CANNOT_DETERMINE_QUALITY. In this situation, you can get a model quality assessment by adding labels to the input dataset and retraining the model.

For information about using labels with your models, see Understanding labeling.

For information about improving the quality of a model, see Best practices with Amazon Lookout for Equipment.

Accessible with the following methods

Method	Description
GET_MODELQUALITY()	Getter for MODELQUALITY, with configurable default
ASK_MODELQUALITY()	Getter for MODELQUALITY w/ exceptions if field has no value
HAS_MODELQUALITY()	Determine if MODELQUALITY has a value

Public Local Types In This Class

Internal table types, representing arrays and maps of this class, are defined as local types:

TT_MODELSUMMARIES

TYPES TT_MODELSUMMARIES TYPE STANDARD TABLE OF REF TO /AWS1/CL_LOEMODELSUMMARY WITH DEFAULT KEY
.