@Generated(value="com.amazonaws:aws-java-sdk-code-generator") public class ModelSummary extends Object implements Serializable, Cloneable, StructuredPojo
Provides information about the specified machine learning model, including dataset and model names and ARNs, as well as status.
Constructor and Description |
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ModelSummary() |
Modifier and Type | Method and Description |
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ModelSummary |
clone() |
boolean |
equals(Object obj) |
Long |
getActiveModelVersion()
The model version that the inference scheduler uses to run an inference execution.
|
String |
getActiveModelVersionArn()
The Amazon Resource Name (ARN) of the model version that is set as active.
|
Date |
getCreatedAt()
The time at which the specific model was created.
|
String |
getDatasetArn()
The Amazon Resource Name (ARN) of the dataset used to create the model.
|
String |
getDatasetName()
The name of the dataset being used for the machine learning model.
|
Long |
getLatestScheduledRetrainingModelVersion()
Indicates the most recent model version that was generated by retraining.
|
Date |
getLatestScheduledRetrainingStartTime()
Indicates the start time of the most recent scheduled retraining run.
|
String |
getLatestScheduledRetrainingStatus()
Indicates the status of the most recent scheduled retraining run.
|
String |
getModelArn()
The Amazon Resource Name (ARN) of the machine learning model.
|
ModelDiagnosticsOutputConfiguration |
getModelDiagnosticsOutputConfiguration() |
String |
getModelName()
The name of the machine learning model.
|
String |
getModelQuality()
Provides a quality assessment for a model that uses labels.
|
Date |
getNextScheduledRetrainingStartDate()
Indicates the date that the next scheduled retraining run will start on.
|
String |
getRetrainingSchedulerStatus()
Indicates the status of the retraining scheduler.
|
String |
getStatus()
Indicates the status of the machine learning model.
|
int |
hashCode() |
void |
marshall(ProtocolMarshaller protocolMarshaller)
Marshalls this structured data using the given
ProtocolMarshaller . |
void |
setActiveModelVersion(Long activeModelVersion)
The model version that the inference scheduler uses to run an inference execution.
|
void |
setActiveModelVersionArn(String activeModelVersionArn)
The Amazon Resource Name (ARN) of the model version that is set as active.
|
void |
setCreatedAt(Date createdAt)
The time at which the specific model was created.
|
void |
setDatasetArn(String datasetArn)
The Amazon Resource Name (ARN) of the dataset used to create the model.
|
void |
setDatasetName(String datasetName)
The name of the dataset being used for the machine learning model.
|
void |
setLatestScheduledRetrainingModelVersion(Long latestScheduledRetrainingModelVersion)
Indicates the most recent model version that was generated by retraining.
|
void |
setLatestScheduledRetrainingStartTime(Date latestScheduledRetrainingStartTime)
Indicates the start time of the most recent scheduled retraining run.
|
void |
setLatestScheduledRetrainingStatus(String latestScheduledRetrainingStatus)
Indicates the status of the most recent scheduled retraining run.
|
void |
setModelArn(String modelArn)
The Amazon Resource Name (ARN) of the machine learning model.
|
void |
setModelDiagnosticsOutputConfiguration(ModelDiagnosticsOutputConfiguration modelDiagnosticsOutputConfiguration) |
void |
setModelName(String modelName)
The name of the machine learning model.
|
void |
setModelQuality(String modelQuality)
Provides a quality assessment for a model that uses labels.
|
void |
setNextScheduledRetrainingStartDate(Date nextScheduledRetrainingStartDate)
Indicates the date that the next scheduled retraining run will start on.
|
void |
setRetrainingSchedulerStatus(String retrainingSchedulerStatus)
Indicates the status of the retraining scheduler.
|
void |
setStatus(String status)
Indicates the status of the machine learning model.
|
String |
toString()
Returns a string representation of this object.
|
ModelSummary |
withActiveModelVersion(Long activeModelVersion)
The model version that the inference scheduler uses to run an inference execution.
|
ModelSummary |
withActiveModelVersionArn(String activeModelVersionArn)
The Amazon Resource Name (ARN) of the model version that is set as active.
|
ModelSummary |
withCreatedAt(Date createdAt)
The time at which the specific model was created.
|
ModelSummary |
withDatasetArn(String datasetArn)
The Amazon Resource Name (ARN) of the dataset used to create the model.
|
ModelSummary |
withDatasetName(String datasetName)
The name of the dataset being used for the machine learning model.
|
ModelSummary |
withLatestScheduledRetrainingModelVersion(Long latestScheduledRetrainingModelVersion)
Indicates the most recent model version that was generated by retraining.
|
ModelSummary |
withLatestScheduledRetrainingStartTime(Date latestScheduledRetrainingStartTime)
Indicates the start time of the most recent scheduled retraining run.
|
ModelSummary |
withLatestScheduledRetrainingStatus(ModelVersionStatus latestScheduledRetrainingStatus)
Indicates the status of the most recent scheduled retraining run.
|
ModelSummary |
withLatestScheduledRetrainingStatus(String latestScheduledRetrainingStatus)
Indicates the status of the most recent scheduled retraining run.
|
ModelSummary |
withModelArn(String modelArn)
The Amazon Resource Name (ARN) of the machine learning model.
|
ModelSummary |
withModelDiagnosticsOutputConfiguration(ModelDiagnosticsOutputConfiguration modelDiagnosticsOutputConfiguration) |
ModelSummary |
withModelName(String modelName)
The name of the machine learning model.
|
ModelSummary |
withModelQuality(ModelQuality modelQuality)
Provides a quality assessment for a model that uses labels.
|
ModelSummary |
withModelQuality(String modelQuality)
Provides a quality assessment for a model that uses labels.
|
ModelSummary |
withNextScheduledRetrainingStartDate(Date nextScheduledRetrainingStartDate)
Indicates the date that the next scheduled retraining run will start on.
|
ModelSummary |
withRetrainingSchedulerStatus(RetrainingSchedulerStatus retrainingSchedulerStatus)
Indicates the status of the retraining scheduler.
|
ModelSummary |
withRetrainingSchedulerStatus(String retrainingSchedulerStatus)
Indicates the status of the retraining scheduler.
|
ModelSummary |
withStatus(ModelStatus status)
Indicates the status of the machine learning model.
|
ModelSummary |
withStatus(String status)
Indicates the status of the machine learning model.
|
public void setModelName(String modelName)
The name of the machine learning model.
modelName
- The name of the machine learning model.public String getModelName()
The name of the machine learning model.
public ModelSummary withModelName(String modelName)
The name of the machine learning model.
modelName
- The name of the machine learning model.public void setModelArn(String modelArn)
The Amazon Resource Name (ARN) of the machine learning model.
modelArn
- The Amazon Resource Name (ARN) of the machine learning model.public String getModelArn()
The Amazon Resource Name (ARN) of the machine learning model.
public ModelSummary withModelArn(String modelArn)
The Amazon Resource Name (ARN) of the machine learning model.
modelArn
- The Amazon Resource Name (ARN) of the machine learning model.public void setDatasetName(String datasetName)
The name of the dataset being used for the machine learning model.
datasetName
- The name of the dataset being used for the machine learning model.public String getDatasetName()
The name of the dataset being used for the machine learning model.
public ModelSummary withDatasetName(String datasetName)
The name of the dataset being used for the machine learning model.
datasetName
- The name of the dataset being used for the machine learning model.public void setDatasetArn(String datasetArn)
The Amazon Resource Name (ARN) of the dataset used to create the model.
datasetArn
- The Amazon Resource Name (ARN) of the dataset used to create the model.public String getDatasetArn()
The Amazon Resource Name (ARN) of the dataset used to create the model.
public ModelSummary withDatasetArn(String datasetArn)
The Amazon Resource Name (ARN) of the dataset used to create the model.
datasetArn
- The Amazon Resource Name (ARN) of the dataset used to create the model.public void setStatus(String status)
Indicates the status of the machine learning model.
status
- Indicates the status of the machine learning model.ModelStatus
public String getStatus()
Indicates the status of the machine learning model.
ModelStatus
public ModelSummary withStatus(String status)
Indicates the status of the machine learning model.
status
- Indicates the status of the machine learning model.ModelStatus
public ModelSummary withStatus(ModelStatus status)
Indicates the status of the machine learning model.
status
- Indicates the status of the machine learning model.ModelStatus
public void setCreatedAt(Date createdAt)
The time at which the specific model was created.
createdAt
- The time at which the specific model was created.public Date getCreatedAt()
The time at which the specific model was created.
public ModelSummary withCreatedAt(Date createdAt)
The time at which the specific model was created.
createdAt
- The time at which the specific model was created.public void setActiveModelVersion(Long activeModelVersion)
The model version that the inference scheduler uses to run an inference execution.
activeModelVersion
- The model version that the inference scheduler uses to run an inference execution.public Long getActiveModelVersion()
The model version that the inference scheduler uses to run an inference execution.
public ModelSummary withActiveModelVersion(Long activeModelVersion)
The model version that the inference scheduler uses to run an inference execution.
activeModelVersion
- The model version that the inference scheduler uses to run an inference execution.public void setActiveModelVersionArn(String 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.
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.public String getActiveModelVersionArn()
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.
public ModelSummary withActiveModelVersionArn(String 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.
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.public void setLatestScheduledRetrainingStatus(String latestScheduledRetrainingStatus)
Indicates the status of the most recent scheduled retraining run.
latestScheduledRetrainingStatus
- Indicates the status of the most recent scheduled retraining run.ModelVersionStatus
public String getLatestScheduledRetrainingStatus()
Indicates the status of the most recent scheduled retraining run.
ModelVersionStatus
public ModelSummary withLatestScheduledRetrainingStatus(String latestScheduledRetrainingStatus)
Indicates the status of the most recent scheduled retraining run.
latestScheduledRetrainingStatus
- Indicates the status of the most recent scheduled retraining run.ModelVersionStatus
public ModelSummary withLatestScheduledRetrainingStatus(ModelVersionStatus latestScheduledRetrainingStatus)
Indicates the status of the most recent scheduled retraining run.
latestScheduledRetrainingStatus
- Indicates the status of the most recent scheduled retraining run.ModelVersionStatus
public void setLatestScheduledRetrainingModelVersion(Long latestScheduledRetrainingModelVersion)
Indicates the most recent model version that was generated by retraining.
latestScheduledRetrainingModelVersion
- Indicates the most recent model version that was generated by retraining.public Long getLatestScheduledRetrainingModelVersion()
Indicates the most recent model version that was generated by retraining.
public ModelSummary withLatestScheduledRetrainingModelVersion(Long latestScheduledRetrainingModelVersion)
Indicates the most recent model version that was generated by retraining.
latestScheduledRetrainingModelVersion
- Indicates the most recent model version that was generated by retraining.public void setLatestScheduledRetrainingStartTime(Date latestScheduledRetrainingStartTime)
Indicates the start time of the most recent scheduled retraining run.
latestScheduledRetrainingStartTime
- Indicates the start time of the most recent scheduled retraining run.public Date getLatestScheduledRetrainingStartTime()
Indicates the start time of the most recent scheduled retraining run.
public ModelSummary withLatestScheduledRetrainingStartTime(Date latestScheduledRetrainingStartTime)
Indicates the start time of the most recent scheduled retraining run.
latestScheduledRetrainingStartTime
- Indicates the start time of the most recent scheduled retraining run.public void setNextScheduledRetrainingStartDate(Date 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.
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.public Date getNextScheduledRetrainingStartDate()
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.
public ModelSummary withNextScheduledRetrainingStartDate(Date 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.
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.public void setRetrainingSchedulerStatus(String retrainingSchedulerStatus)
Indicates the status of the retraining scheduler.
retrainingSchedulerStatus
- Indicates the status of the retraining scheduler.RetrainingSchedulerStatus
public String getRetrainingSchedulerStatus()
Indicates the status of the retraining scheduler.
RetrainingSchedulerStatus
public ModelSummary withRetrainingSchedulerStatus(String retrainingSchedulerStatus)
Indicates the status of the retraining scheduler.
retrainingSchedulerStatus
- Indicates the status of the retraining scheduler.RetrainingSchedulerStatus
public ModelSummary withRetrainingSchedulerStatus(RetrainingSchedulerStatus retrainingSchedulerStatus)
Indicates the status of the retraining scheduler.
retrainingSchedulerStatus
- Indicates the status of the retraining scheduler.RetrainingSchedulerStatus
public void setModelDiagnosticsOutputConfiguration(ModelDiagnosticsOutputConfiguration modelDiagnosticsOutputConfiguration)
modelDiagnosticsOutputConfiguration
- public ModelDiagnosticsOutputConfiguration getModelDiagnosticsOutputConfiguration()
public ModelSummary withModelDiagnosticsOutputConfiguration(ModelDiagnosticsOutputConfiguration modelDiagnosticsOutputConfiguration)
modelDiagnosticsOutputConfiguration
- public void setModelQuality(String 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.
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.
ModelQuality
public String getModelQuality()
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.
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.
ModelQuality
public ModelSummary withModelQuality(String 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.
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.
ModelQuality
public ModelSummary withModelQuality(ModelQuality 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.
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.
ModelQuality
public String toString()
toString
in class Object
Object.toString()
public ModelSummary clone()
public void marshall(ProtocolMarshaller protocolMarshaller)
StructuredPojo
ProtocolMarshaller
.marshall
in interface StructuredPojo
protocolMarshaller
- Implementation of ProtocolMarshaller
used to marshall this object's data.