Uses of Class
com.amazonaws.services.neptunedata.model.StartMLModelTransformJobRequest

Packages that use StartMLModelTransformJobRequest

Package	Description
com.amazonaws.services.neptunedata	Neptune Data API
com.amazonaws.services.neptunedata.model

Uses of StartMLModelTransformJobRequest in com.amazonaws.services.neptunedata

Methods in com.amazonaws.services.neptunedata with parameters of type StartMLModelTransformJobRequest

Modifier and Type	Method and Description
StartMLModelTransformJobResult	AbstractAmazonNeptunedata.startMLModelTransformJob(StartMLModelTransformJobRequest request)
StartMLModelTransformJobResult	AmazonNeptunedataClient.startMLModelTransformJob(StartMLModelTransformJobRequest request) Creates a new model transform job.
StartMLModelTransformJobResult	AmazonNeptunedata.startMLModelTransformJob(StartMLModelTransformJobRequest startMLModelTransformJobRequest) Creates a new model transform job.
Future<StartMLModelTransformJobResult>	AmazonNeptunedataAsyncClient.startMLModelTransformJobAsync(StartMLModelTransformJobRequest request)
Future<StartMLModelTransformJobResult>	AbstractAmazonNeptunedataAsync.startMLModelTransformJobAsync(StartMLModelTransformJobRequest request)
Future<StartMLModelTransformJobResult>	AmazonNeptunedataAsync.startMLModelTransformJobAsync(StartMLModelTransformJobRequest startMLModelTransformJobRequest) Creates a new model transform job.
Future<StartMLModelTransformJobResult>	AmazonNeptunedataAsyncClient.startMLModelTransformJobAsync(StartMLModelTransformJobRequest request, AsyncHandler<StartMLModelTransformJobRequest,StartMLModelTransformJobResult> asyncHandler)
Future<StartMLModelTransformJobResult>	AbstractAmazonNeptunedataAsync.startMLModelTransformJobAsync(StartMLModelTransformJobRequest request, AsyncHandler<StartMLModelTransformJobRequest,StartMLModelTransformJobResult> asyncHandler)
Future<StartMLModelTransformJobResult>	AmazonNeptunedataAsync.startMLModelTransformJobAsync(StartMLModelTransformJobRequest startMLModelTransformJobRequest, AsyncHandler<StartMLModelTransformJobRequest,StartMLModelTransformJobResult> asyncHandler) Creates a new model transform job.

Method parameters in com.amazonaws.services.neptunedata with type arguments of type StartMLModelTransformJobRequest

Modifier and Type	Method and Description
Future<StartMLModelTransformJobResult>	AmazonNeptunedataAsyncClient.startMLModelTransformJobAsync(StartMLModelTransformJobRequest request, AsyncHandler<StartMLModelTransformJobRequest,StartMLModelTransformJobResult> asyncHandler)
Future<StartMLModelTransformJobResult>	AbstractAmazonNeptunedataAsync.startMLModelTransformJobAsync(StartMLModelTransformJobRequest request, AsyncHandler<StartMLModelTransformJobRequest,StartMLModelTransformJobResult> asyncHandler)
Future<StartMLModelTransformJobResult>	AmazonNeptunedataAsync.startMLModelTransformJobAsync(StartMLModelTransformJobRequest startMLModelTransformJobRequest, AsyncHandler<StartMLModelTransformJobRequest,StartMLModelTransformJobResult> asyncHandler) Creates a new model transform job.

Uses of StartMLModelTransformJobRequest in com.amazonaws.services.neptunedata.model

Methods in com.amazonaws.services.neptunedata.model that return StartMLModelTransformJobRequest

Modifier and Type	Method and Description
StartMLModelTransformJobRequest	StartMLModelTransformJobRequest.clone()
StartMLModelTransformJobRequest	StartMLModelTransformJobRequest.withBaseProcessingInstanceType(String baseProcessingInstanceType) The type of ML instance used in preparing and managing training of ML models.
StartMLModelTransformJobRequest	StartMLModelTransformJobRequest.withBaseProcessingInstanceVolumeSizeInGB(Integer baseProcessingInstanceVolumeSizeInGB) The disk volume size of the training instance in gigabytes.
StartMLModelTransformJobRequest	StartMLModelTransformJobRequest.withCustomModelTransformParameters(CustomModelTransformParameters customModelTransformParameters) Configuration information for a model transform using a custom model.
StartMLModelTransformJobRequest	StartMLModelTransformJobRequest.withDataProcessingJobId(String dataProcessingJobId) The job ID of a completed data-processing job.
StartMLModelTransformJobRequest	StartMLModelTransformJobRequest.withId(String id) A unique identifier for the new job.
StartMLModelTransformJobRequest	StartMLModelTransformJobRequest.withMlModelTrainingJobId(String mlModelTrainingJobId) The job ID of a completed model-training job.
StartMLModelTransformJobRequest	StartMLModelTransformJobRequest.withModelTransformOutputS3Location(String modelTransformOutputS3Location) The location in Amazon S3 where the model artifacts are to be stored.
StartMLModelTransformJobRequest	StartMLModelTransformJobRequest.withNeptuneIamRoleArn(String neptuneIamRoleArn) The ARN of an IAM role that provides Neptune access to SageMaker and Amazon S3 resources.
StartMLModelTransformJobRequest	StartMLModelTransformJobRequest.withS3OutputEncryptionKMSKey(String s3OutputEncryptionKMSKey) The Amazon Key Management Service (KMS) key that SageMaker uses to encrypt the output of the processing job.
StartMLModelTransformJobRequest	StartMLModelTransformJobRequest.withSagemakerIamRoleArn(String sagemakerIamRoleArn) The ARN of an IAM role for SageMaker execution.
StartMLModelTransformJobRequest	StartMLModelTransformJobRequest.withSecurityGroupIds(Collection<String> securityGroupIds) The VPC security group IDs.
StartMLModelTransformJobRequest	StartMLModelTransformJobRequest.withSecurityGroupIds(String... securityGroupIds) The VPC security group IDs.
StartMLModelTransformJobRequest	StartMLModelTransformJobRequest.withSubnets(Collection<String> subnets) The IDs of the subnets in the Neptune VPC.
StartMLModelTransformJobRequest	StartMLModelTransformJobRequest.withSubnets(String... subnets) The IDs of the subnets in the Neptune VPC.
StartMLModelTransformJobRequest	StartMLModelTransformJobRequest.withTrainingJobName(String trainingJobName) The name of a completed SageMaker training job.
StartMLModelTransformJobRequest	StartMLModelTransformJobRequest.withVolumeEncryptionKMSKey(String volumeEncryptionKMSKey) The Amazon Key Management Service (KMS) key that SageMaker uses to encrypt data on the storage volume attached to the ML compute instances that run the training job.