AWS SDK Version 3 for .NET
API Reference

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Container for the parameters to the CreateAutoMLJobV2 operation. Creates an Autopilot job also referred to as Autopilot experiment or AutoML job V2.

CreateAutoMLJobV2 and DescribeAutoMLJobV2 are new versions of CreateAutoMLJob and DescribeAutoMLJob which offer backward compatibility.

CreateAutoMLJobV2 can manage tabular problem types identical to those of its previous version CreateAutoMLJob, as well as time-series forecasting, non-tabular problem types such as image or text classification, and text generation (LLMs fine-tuning).

Find guidelines about how to migrate a CreateAutoMLJob to CreateAutoMLJobV2 in Migrate a CreateAutoMLJob to CreateAutoMLJobV2.

For the list of available problem types supported by CreateAutoMLJobV2, see AutoMLProblemTypeConfig.

You can find the best-performing model after you run an AutoML job V2 by calling DescribeAutoMLJobV2.

Inheritance Hierarchy

System.Object
  Amazon.Runtime.AmazonWebServiceRequest
    Amazon.SageMaker.AmazonSageMakerRequest
      Amazon.SageMaker.Model.CreateAutoMLJobV2Request

Namespace: Amazon.SageMaker.Model
Assembly: AWSSDK.SageMaker.dll
Version: 3.x.y.z

Syntax

C# 
public class CreateAutoMLJobV2Request : AmazonSageMakerRequest
         IAmazonWebServiceRequest

The CreateAutoMLJobV2Request type exposes the following members

Constructors

NameDescription
Public Method CreateAutoMLJobV2Request()

Properties

NameTypeDescription
Public Property AutoMLJobInputDataConfig System.Collections.Generic.List<Amazon.SageMaker.Model.AutoMLJobChannel>

Gets and sets the property AutoMLJobInputDataConfig.

An array of channel objects describing the input data and their location. Each channel is a named input source. Similar to the InputDataConfig attribute in the CreateAutoMLJob input parameters. The supported formats depend on the problem type:

  • For tabular problem types: S3Prefix, ManifestFile.

  • For image classification: S3Prefix, ManifestFile, AugmentedManifestFile.

  • For text classification: S3Prefix.

  • For time-series forecasting: S3Prefix.

  • For text generation (LLMs fine-tuning): S3Prefix.

Public Property AutoMLJobName System.String

Gets and sets the property AutoMLJobName.

Identifies an Autopilot job. The name must be unique to your account and is case insensitive.

Public Property AutoMLJobObjective Amazon.SageMaker.Model.AutoMLJobObjective

Gets and sets the property AutoMLJobObjective.

Specifies a metric to minimize or maximize as the objective of a job. If not specified, the default objective metric depends on the problem type. For the list of default values per problem type, see AutoMLJobObjective.

  • For tabular problem types: You must either provide both the AutoMLJobObjective and indicate the type of supervised learning problem in AutoMLProblemTypeConfig (TabularJobConfig.ProblemType), or none at all.

  • For text generation problem types (LLMs fine-tuning): Fine-tuning language models in Autopilot does not require setting the AutoMLJobObjective field. Autopilot fine-tunes LLMs without requiring multiple candidates to be trained and evaluated. Instead, using your dataset, Autopilot directly fine-tunes your target model to enhance a default objective metric, the cross-entropy loss. After fine-tuning a language model, you can evaluate the quality of its generated text using different metrics. For a list of the available metrics, see Metrics for fine-tuning LLMs in Autopilot.

Public Property AutoMLProblemTypeConfig Amazon.SageMaker.Model.AutoMLProblemTypeConfig

Gets and sets the property AutoMLProblemTypeConfig.

Defines the configuration settings of one of the supported problem types.

Public Property DataSplitConfig Amazon.SageMaker.Model.AutoMLDataSplitConfig

Gets and sets the property DataSplitConfig.

This structure specifies how to split the data into train and validation datasets.

The validation and training datasets must contain the same headers. For jobs created by calling CreateAutoMLJob, the validation dataset must be less than 2 GB in size.

This attribute must not be set for the time-series forecasting problem type, as Autopilot automatically splits the input dataset into training and validation sets.

Public Property ModelDeployConfig Amazon.SageMaker.Model.ModelDeployConfig

Gets and sets the property ModelDeployConfig.

Specifies how to generate the endpoint name for an automatic one-click Autopilot model deployment.

Public Property OutputDataConfig Amazon.SageMaker.Model.AutoMLOutputDataConfig

Gets and sets the property OutputDataConfig.

Provides information about encryption and the Amazon S3 output path needed to store artifacts from an AutoML job.

Public Property RoleArn System.String

Gets and sets the property RoleArn.

The ARN of the role that is used to access the data.

Public Property SecurityConfig Amazon.SageMaker.Model.AutoMLSecurityConfig

Gets and sets the property SecurityConfig.

The security configuration for traffic encryption or Amazon VPC settings.

Public Property Tags System.Collections.Generic.List<Amazon.SageMaker.Model.Tag>

Gets and sets the property Tags.

An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, such as by purpose, owner, or environment. For more information, see Tagging Amazon Web ServicesResources. Tag keys must be unique per resource.

Version Information

.NET:
Supported in: 8.0 and newer, Core 3.1

.NET Standard:
Supported in: 2.0

.NET Framework:
Supported in: 4.5 and newer, 3.5