@Generated(value="com.amazonaws:aws-java-sdk-code-generator") public class CreateInferenceExperimentRequest extends AmazonWebServiceRequest implements Serializable, Cloneable
NOOP| Constructor and Description |
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CreateInferenceExperimentRequest() |
| Modifier and Type | Method and Description |
|---|---|
CreateInferenceExperimentRequest |
clone()
Creates a shallow clone of this object for all fields except the handler context.
|
boolean |
equals(Object obj) |
InferenceExperimentDataStorageConfig |
getDataStorageConfig()
The Amazon S3 location and configuration for storing inference request and response data.
|
String |
getDescription()
A description for the inference experiment.
|
String |
getEndpointName()
The name of the Amazon SageMaker endpoint on which you want to run the inference experiment.
|
String |
getKmsKey()
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to
encrypt data on the storage volume attached to the ML compute instance that hosts the endpoint.
|
List<ModelVariantConfig> |
getModelVariants()
An array of
ModelVariantConfig objects. |
String |
getName()
The name for the inference experiment.
|
String |
getRoleArn()
The ARN of the IAM role that Amazon SageMaker can assume to access model artifacts and container images, and
manage Amazon SageMaker Inference endpoints for model deployment.
|
InferenceExperimentSchedule |
getSchedule()
The duration for which you want the inference experiment to run.
|
ShadowModeConfig |
getShadowModeConfig()
The configuration of
ShadowMode inference experiment type. |
List<Tag> |
getTags()
Array of key-value pairs.
|
String |
getType()
The type of the inference experiment that you want to run.
|
int |
hashCode() |
void |
setDataStorageConfig(InferenceExperimentDataStorageConfig dataStorageConfig)
The Amazon S3 location and configuration for storing inference request and response data.
|
void |
setDescription(String description)
A description for the inference experiment.
|
void |
setEndpointName(String endpointName)
The name of the Amazon SageMaker endpoint on which you want to run the inference experiment.
|
void |
setKmsKey(String kmsKey)
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to
encrypt data on the storage volume attached to the ML compute instance that hosts the endpoint.
|
void |
setModelVariants(Collection<ModelVariantConfig> modelVariants)
An array of
ModelVariantConfig objects. |
void |
setName(String name)
The name for the inference experiment.
|
void |
setRoleArn(String roleArn)
The ARN of the IAM role that Amazon SageMaker can assume to access model artifacts and container images, and
manage Amazon SageMaker Inference endpoints for model deployment.
|
void |
setSchedule(InferenceExperimentSchedule schedule)
The duration for which you want the inference experiment to run.
|
void |
setShadowModeConfig(ShadowModeConfig shadowModeConfig)
The configuration of
ShadowMode inference experiment type. |
void |
setTags(Collection<Tag> tags)
Array of key-value pairs.
|
void |
setType(String type)
The type of the inference experiment that you want to run.
|
String |
toString()
Returns a string representation of this object.
|
CreateInferenceExperimentRequest |
withDataStorageConfig(InferenceExperimentDataStorageConfig dataStorageConfig)
The Amazon S3 location and configuration for storing inference request and response data.
|
CreateInferenceExperimentRequest |
withDescription(String description)
A description for the inference experiment.
|
CreateInferenceExperimentRequest |
withEndpointName(String endpointName)
The name of the Amazon SageMaker endpoint on which you want to run the inference experiment.
|
CreateInferenceExperimentRequest |
withKmsKey(String kmsKey)
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to
encrypt data on the storage volume attached to the ML compute instance that hosts the endpoint.
|
CreateInferenceExperimentRequest |
withModelVariants(Collection<ModelVariantConfig> modelVariants)
An array of
ModelVariantConfig objects. |
CreateInferenceExperimentRequest |
withModelVariants(ModelVariantConfig... modelVariants)
An array of
ModelVariantConfig objects. |
CreateInferenceExperimentRequest |
withName(String name)
The name for the inference experiment.
|
CreateInferenceExperimentRequest |
withRoleArn(String roleArn)
The ARN of the IAM role that Amazon SageMaker can assume to access model artifacts and container images, and
manage Amazon SageMaker Inference endpoints for model deployment.
|
CreateInferenceExperimentRequest |
withSchedule(InferenceExperimentSchedule schedule)
The duration for which you want the inference experiment to run.
|
CreateInferenceExperimentRequest |
withShadowModeConfig(ShadowModeConfig shadowModeConfig)
The configuration of
ShadowMode inference experiment type. |
CreateInferenceExperimentRequest |
withTags(Collection<Tag> tags)
Array of key-value pairs.
|
CreateInferenceExperimentRequest |
withTags(Tag... tags)
Array of key-value pairs.
|
CreateInferenceExperimentRequest |
withType(InferenceExperimentType type)
The type of the inference experiment that you want to run.
|
CreateInferenceExperimentRequest |
withType(String type)
The type of the inference experiment that you want to run.
|
addHandlerContext, getCloneRoot, getCloneSource, getCustomQueryParameters, getCustomRequestHeaders, getGeneralProgressListener, getHandlerContext, getReadLimit, getRequestClientOptions, getRequestCredentials, getRequestCredentialsProvider, getRequestMetricCollector, getSdkClientExecutionTimeout, getSdkRequestTimeout, putCustomQueryParameter, putCustomRequestHeader, setGeneralProgressListener, setRequestCredentials, setRequestCredentialsProvider, setRequestMetricCollector, setSdkClientExecutionTimeout, setSdkRequestTimeout, withGeneralProgressListener, withRequestCredentialsProvider, withRequestMetricCollector, withSdkClientExecutionTimeout, withSdkRequestTimeoutpublic void setName(String name)
The name for the inference experiment.
name - The name for the inference experiment.public String getName()
The name for the inference experiment.
public CreateInferenceExperimentRequest withName(String name)
The name for the inference experiment.
name - The name for the inference experiment.public void setType(String type)
The type of the inference experiment that you want to run. The following types of experiments are possible:
ShadowMode: You can use this type to validate a shadow variant. For more information, see Shadow tests.
type - The type of the inference experiment that you want to run. The following types of experiments are
possible:
ShadowMode: You can use this type to validate a shadow variant. For more information, see Shadow tests.
InferenceExperimentTypepublic String getType()
The type of the inference experiment that you want to run. The following types of experiments are possible:
ShadowMode: You can use this type to validate a shadow variant. For more information, see Shadow tests.
ShadowMode: You can use this type to validate a shadow variant. For more information, see Shadow tests.
InferenceExperimentTypepublic CreateInferenceExperimentRequest withType(String type)
The type of the inference experiment that you want to run. The following types of experiments are possible:
ShadowMode: You can use this type to validate a shadow variant. For more information, see Shadow tests.
type - The type of the inference experiment that you want to run. The following types of experiments are
possible:
ShadowMode: You can use this type to validate a shadow variant. For more information, see Shadow tests.
InferenceExperimentTypepublic CreateInferenceExperimentRequest withType(InferenceExperimentType type)
The type of the inference experiment that you want to run. The following types of experiments are possible:
ShadowMode: You can use this type to validate a shadow variant. For more information, see Shadow tests.
type - The type of the inference experiment that you want to run. The following types of experiments are
possible:
ShadowMode: You can use this type to validate a shadow variant. For more information, see Shadow tests.
InferenceExperimentTypepublic void setSchedule(InferenceExperimentSchedule schedule)
The duration for which you want the inference experiment to run. If you don't specify this field, the experiment automatically starts immediately upon creation and concludes after 7 days.
schedule - The duration for which you want the inference experiment to run. If you don't specify this field, the
experiment automatically starts immediately upon creation and concludes after 7 days.public InferenceExperimentSchedule getSchedule()
The duration for which you want the inference experiment to run. If you don't specify this field, the experiment automatically starts immediately upon creation and concludes after 7 days.
public CreateInferenceExperimentRequest withSchedule(InferenceExperimentSchedule schedule)
The duration for which you want the inference experiment to run. If you don't specify this field, the experiment automatically starts immediately upon creation and concludes after 7 days.
schedule - The duration for which you want the inference experiment to run. If you don't specify this field, the
experiment automatically starts immediately upon creation and concludes after 7 days.public void setDescription(String description)
A description for the inference experiment.
description - A description for the inference experiment.public String getDescription()
A description for the inference experiment.
public CreateInferenceExperimentRequest withDescription(String description)
A description for the inference experiment.
description - A description for the inference experiment.public void setRoleArn(String roleArn)
The ARN of the IAM role that Amazon SageMaker can assume to access model artifacts and container images, and manage Amazon SageMaker Inference endpoints for model deployment.
roleArn - The ARN of the IAM role that Amazon SageMaker can assume to access model artifacts and container images,
and manage Amazon SageMaker Inference endpoints for model deployment.public String getRoleArn()
The ARN of the IAM role that Amazon SageMaker can assume to access model artifacts and container images, and manage Amazon SageMaker Inference endpoints for model deployment.
public CreateInferenceExperimentRequest withRoleArn(String roleArn)
The ARN of the IAM role that Amazon SageMaker can assume to access model artifacts and container images, and manage Amazon SageMaker Inference endpoints for model deployment.
roleArn - The ARN of the IAM role that Amazon SageMaker can assume to access model artifacts and container images,
and manage Amazon SageMaker Inference endpoints for model deployment.public void setEndpointName(String endpointName)
The name of the Amazon SageMaker endpoint on which you want to run the inference experiment.
endpointName - The name of the Amazon SageMaker endpoint on which you want to run the inference experiment.public String getEndpointName()
The name of the Amazon SageMaker endpoint on which you want to run the inference experiment.
public CreateInferenceExperimentRequest withEndpointName(String endpointName)
The name of the Amazon SageMaker endpoint on which you want to run the inference experiment.
endpointName - The name of the Amazon SageMaker endpoint on which you want to run the inference experiment.public List<ModelVariantConfig> getModelVariants()
An array of ModelVariantConfig objects. There is one for each variant in the inference experiment.
Each ModelVariantConfig object in the array describes the infrastructure configuration for the
corresponding variant.
ModelVariantConfig objects. There is one for each variant in the inference
experiment. Each ModelVariantConfig object in the array describes the infrastructure
configuration for the corresponding variant.public void setModelVariants(Collection<ModelVariantConfig> modelVariants)
An array of ModelVariantConfig objects. There is one for each variant in the inference experiment.
Each ModelVariantConfig object in the array describes the infrastructure configuration for the
corresponding variant.
modelVariants - An array of ModelVariantConfig objects. There is one for each variant in the inference
experiment. Each ModelVariantConfig object in the array describes the infrastructure
configuration for the corresponding variant.public CreateInferenceExperimentRequest withModelVariants(ModelVariantConfig... modelVariants)
An array of ModelVariantConfig objects. There is one for each variant in the inference experiment.
Each ModelVariantConfig object in the array describes the infrastructure configuration for the
corresponding variant.
NOTE: This method appends the values to the existing list (if any). Use
setModelVariants(java.util.Collection) or withModelVariants(java.util.Collection) if you want
to override the existing values.
modelVariants - An array of ModelVariantConfig objects. There is one for each variant in the inference
experiment. Each ModelVariantConfig object in the array describes the infrastructure
configuration for the corresponding variant.public CreateInferenceExperimentRequest withModelVariants(Collection<ModelVariantConfig> modelVariants)
An array of ModelVariantConfig objects. There is one for each variant in the inference experiment.
Each ModelVariantConfig object in the array describes the infrastructure configuration for the
corresponding variant.
modelVariants - An array of ModelVariantConfig objects. There is one for each variant in the inference
experiment. Each ModelVariantConfig object in the array describes the infrastructure
configuration for the corresponding variant.public void setDataStorageConfig(InferenceExperimentDataStorageConfig dataStorageConfig)
The Amazon S3 location and configuration for storing inference request and response data.
This is an optional parameter that you can use for data capture. For more information, see Capture data.
dataStorageConfig - The Amazon S3 location and configuration for storing inference request and response data.
This is an optional parameter that you can use for data capture. For more information, see Capture data.
public InferenceExperimentDataStorageConfig getDataStorageConfig()
The Amazon S3 location and configuration for storing inference request and response data.
This is an optional parameter that you can use for data capture. For more information, see Capture data.
This is an optional parameter that you can use for data capture. For more information, see Capture data.
public CreateInferenceExperimentRequest withDataStorageConfig(InferenceExperimentDataStorageConfig dataStorageConfig)
The Amazon S3 location and configuration for storing inference request and response data.
This is an optional parameter that you can use for data capture. For more information, see Capture data.
dataStorageConfig - The Amazon S3 location and configuration for storing inference request and response data.
This is an optional parameter that you can use for data capture. For more information, see Capture data.
public void setShadowModeConfig(ShadowModeConfig shadowModeConfig)
The configuration of ShadowMode inference experiment type. Use this field to specify a production
variant which takes all the inference requests, and a shadow variant to which Amazon SageMaker replicates a
percentage of the inference requests. For the shadow variant also specify the percentage of requests that Amazon
SageMaker replicates.
shadowModeConfig - The configuration of ShadowMode inference experiment type. Use this field to specify a
production variant which takes all the inference requests, and a shadow variant to which Amazon SageMaker
replicates a percentage of the inference requests. For the shadow variant also specify the percentage of
requests that Amazon SageMaker replicates.public ShadowModeConfig getShadowModeConfig()
The configuration of ShadowMode inference experiment type. Use this field to specify a production
variant which takes all the inference requests, and a shadow variant to which Amazon SageMaker replicates a
percentage of the inference requests. For the shadow variant also specify the percentage of requests that Amazon
SageMaker replicates.
ShadowMode inference experiment type. Use this field to specify a
production variant which takes all the inference requests, and a shadow variant to which Amazon SageMaker
replicates a percentage of the inference requests. For the shadow variant also specify the percentage of
requests that Amazon SageMaker replicates.public CreateInferenceExperimentRequest withShadowModeConfig(ShadowModeConfig shadowModeConfig)
The configuration of ShadowMode inference experiment type. Use this field to specify a production
variant which takes all the inference requests, and a shadow variant to which Amazon SageMaker replicates a
percentage of the inference requests. For the shadow variant also specify the percentage of requests that Amazon
SageMaker replicates.
shadowModeConfig - The configuration of ShadowMode inference experiment type. Use this field to specify a
production variant which takes all the inference requests, and a shadow variant to which Amazon SageMaker
replicates a percentage of the inference requests. For the shadow variant also specify the percentage of
requests that Amazon SageMaker replicates.public void setKmsKey(String kmsKey)
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to
encrypt data on the storage volume attached to the ML compute instance that hosts the endpoint. The
KmsKey can be any of the following formats:
KMS key ID
"1234abcd-12ab-34cd-56ef-1234567890ab"
Amazon Resource Name (ARN) of a KMS key
"arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
KMS key Alias
"alias/ExampleAlias"
Amazon Resource Name (ARN) of a KMS key Alias
"arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias"
If you use a KMS key ID or an alias of your KMS key, the Amazon SageMaker execution role must include permissions
to call kms:Encrypt. If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key
for Amazon S3 for your role's account. Amazon SageMaker uses server-side encryption with KMS managed keys for
OutputDataConfig. If you use a bucket policy with an s3:PutObject permission that only
allows objects with server-side encryption, set the condition key of s3:x-amz-server-side-encryption
to "aws:kms". For more information, see KMS managed Encryption Keys in
the Amazon Simple Storage Service Developer Guide.
The KMS key policy must grant permission to the IAM role that you specify in your CreateEndpoint and
UpdateEndpoint requests. For more information, see Using Key Policies in Amazon Web
Services KMS in the Amazon Web Services Key Management Service Developer Guide.
kmsKey - The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to
encrypt data on the storage volume attached to the ML compute instance that hosts the endpoint. The
KmsKey can be any of the following formats:
KMS key ID
"1234abcd-12ab-34cd-56ef-1234567890ab"
Amazon Resource Name (ARN) of a KMS key
"arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
KMS key Alias
"alias/ExampleAlias"
Amazon Resource Name (ARN) of a KMS key Alias
"arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias"
If you use a KMS key ID or an alias of your KMS key, the Amazon SageMaker execution role must include
permissions to call kms:Encrypt. If you don't provide a KMS key ID, Amazon SageMaker uses the
default KMS key for Amazon S3 for your role's account. Amazon SageMaker uses server-side encryption with
KMS managed keys for OutputDataConfig. If you use a bucket policy with an
s3:PutObject permission that only allows objects with server-side encryption, set the
condition key of s3:x-amz-server-side-encryption to "aws:kms". For more
information, see KMS
managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.
The KMS key policy must grant permission to the IAM role that you specify in your
CreateEndpoint and UpdateEndpoint requests. For more information, see Using Key Policies in
Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide.
public String getKmsKey()
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to
encrypt data on the storage volume attached to the ML compute instance that hosts the endpoint. The
KmsKey can be any of the following formats:
KMS key ID
"1234abcd-12ab-34cd-56ef-1234567890ab"
Amazon Resource Name (ARN) of a KMS key
"arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
KMS key Alias
"alias/ExampleAlias"
Amazon Resource Name (ARN) of a KMS key Alias
"arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias"
If you use a KMS key ID or an alias of your KMS key, the Amazon SageMaker execution role must include permissions
to call kms:Encrypt. If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key
for Amazon S3 for your role's account. Amazon SageMaker uses server-side encryption with KMS managed keys for
OutputDataConfig. If you use a bucket policy with an s3:PutObject permission that only
allows objects with server-side encryption, set the condition key of s3:x-amz-server-side-encryption
to "aws:kms". For more information, see KMS managed Encryption Keys in
the Amazon Simple Storage Service Developer Guide.
The KMS key policy must grant permission to the IAM role that you specify in your CreateEndpoint and
UpdateEndpoint requests. For more information, see Using Key Policies in Amazon Web
Services KMS in the Amazon Web Services Key Management Service Developer Guide.
KmsKey can be any of the following formats:
KMS key ID
"1234abcd-12ab-34cd-56ef-1234567890ab"
Amazon Resource Name (ARN) of a KMS key
"arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
KMS key Alias
"alias/ExampleAlias"
Amazon Resource Name (ARN) of a KMS key Alias
"arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias"
If you use a KMS key ID or an alias of your KMS key, the Amazon SageMaker execution role must include
permissions to call kms:Encrypt. If you don't provide a KMS key ID, Amazon SageMaker uses
the default KMS key for Amazon S3 for your role's account. Amazon SageMaker uses server-side encryption
with KMS managed keys for OutputDataConfig. If you use a bucket policy with an
s3:PutObject permission that only allows objects with server-side encryption, set the
condition key of s3:x-amz-server-side-encryption to "aws:kms". For more
information, see KMS
managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.
The KMS key policy must grant permission to the IAM role that you specify in your
CreateEndpoint and UpdateEndpoint requests. For more information, see Using Key Policies in
Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide.
public CreateInferenceExperimentRequest withKmsKey(String kmsKey)
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to
encrypt data on the storage volume attached to the ML compute instance that hosts the endpoint. The
KmsKey can be any of the following formats:
KMS key ID
"1234abcd-12ab-34cd-56ef-1234567890ab"
Amazon Resource Name (ARN) of a KMS key
"arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
KMS key Alias
"alias/ExampleAlias"
Amazon Resource Name (ARN) of a KMS key Alias
"arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias"
If you use a KMS key ID or an alias of your KMS key, the Amazon SageMaker execution role must include permissions
to call kms:Encrypt. If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key
for Amazon S3 for your role's account. Amazon SageMaker uses server-side encryption with KMS managed keys for
OutputDataConfig. If you use a bucket policy with an s3:PutObject permission that only
allows objects with server-side encryption, set the condition key of s3:x-amz-server-side-encryption
to "aws:kms". For more information, see KMS managed Encryption Keys in
the Amazon Simple Storage Service Developer Guide.
The KMS key policy must grant permission to the IAM role that you specify in your CreateEndpoint and
UpdateEndpoint requests. For more information, see Using Key Policies in Amazon Web
Services KMS in the Amazon Web Services Key Management Service Developer Guide.
kmsKey - The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to
encrypt data on the storage volume attached to the ML compute instance that hosts the endpoint. The
KmsKey can be any of the following formats:
KMS key ID
"1234abcd-12ab-34cd-56ef-1234567890ab"
Amazon Resource Name (ARN) of a KMS key
"arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
KMS key Alias
"alias/ExampleAlias"
Amazon Resource Name (ARN) of a KMS key Alias
"arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias"
If you use a KMS key ID or an alias of your KMS key, the Amazon SageMaker execution role must include
permissions to call kms:Encrypt. If you don't provide a KMS key ID, Amazon SageMaker uses the
default KMS key for Amazon S3 for your role's account. Amazon SageMaker uses server-side encryption with
KMS managed keys for OutputDataConfig. If you use a bucket policy with an
s3:PutObject permission that only allows objects with server-side encryption, set the
condition key of s3:x-amz-server-side-encryption to "aws:kms". For more
information, see KMS
managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.
The KMS key policy must grant permission to the IAM role that you specify in your
CreateEndpoint and UpdateEndpoint requests. For more information, see Using Key Policies in
Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide.
public List<Tag> getTags()
Array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging your Amazon Web Services Resources.
public void setTags(Collection<Tag> tags)
Array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging your Amazon Web Services Resources.
tags - Array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different
ways, for example, by purpose, owner, or environment. For more information, see Tagging your Amazon Web Services
Resources.public CreateInferenceExperimentRequest withTags(Tag... tags)
Array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging your Amazon Web Services Resources.
NOTE: This method appends the values to the existing list (if any). Use
setTags(java.util.Collection) or withTags(java.util.Collection) if you want to override the
existing values.
tags - Array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different
ways, for example, by purpose, owner, or environment. For more information, see Tagging your Amazon Web Services
Resources.public CreateInferenceExperimentRequest withTags(Collection<Tag> tags)
Array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging your Amazon Web Services Resources.
tags - Array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different
ways, for example, by purpose, owner, or environment. For more information, see Tagging your Amazon Web Services
Resources.public String toString()
toString in class ObjectObject.toString()public CreateInferenceExperimentRequest clone()
AmazonWebServiceRequestclone in class AmazonWebServiceRequestObject.clone()