Struct CreateAutoMLJobV2FluentBuilder

pub struct CreateAutoMLJobV2FluentBuilder { /* private fields */ }

Fluent builder constructing a request to CreateAutoMLJobV2.

Creates an Autopilot job also referred to as Autopilot experiment or AutoML job V2.

An AutoML job in SageMaker AI is a fully automated process that allows you to build machine learning models with minimal effort and machine learning expertise. When initiating an AutoML job, you provide your data and optionally specify parameters tailored to your use case. SageMaker AI then automates the entire model development lifecycle, including data preprocessing, model training, tuning, and evaluation. AutoML jobs are designed to simplify and accelerate the model building process by automating various tasks and exploring different combinations of machine learning algorithms, data preprocessing techniques, and hyperparameter values. The output of an AutoML job comprises one or more trained models ready for deployment and inference. Additionally, SageMaker AI AutoML jobs generate a candidate model leaderboard, allowing you to select the best-performing model for deployment.

For more information about AutoML jobs, see https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-automate-model-development.html in the SageMaker AI developer guide.

AutoML jobs V2 support various problem types such as regression, binary, and multiclass classification with tabular data, text and image classification, time-series forecasting, and fine-tuning of large language models (LLMs) for text generation.

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.

Implementations

impl CreateAutoMLJobV2FluentBuilder

pub fn as_input(&self) -> &CreateAutoMlJobV2InputBuilder

Access the CreateAutoMLJobV2 as a reference.

pub async fn send( self, ) -> Result<CreateAutoMlJobV2Output, SdkError<CreateAutoMLJobV2Error, HttpResponse>>

Sends the request and returns the response.

If an error occurs, an SdkError will be returned with additional details that can be matched against.

By default, any retryable failures will be retried twice. Retry behavior is configurable with the RetryConfig, which can be set when configuring the client.

pub fn customize( self, ) -> CustomizableOperation<CreateAutoMlJobV2Output, CreateAutoMLJobV2Error, Self>

Consumes this builder, creating a customizable operation that can be modified before being sent.

pub fn auto_ml_job_name(self, input: impl Into<String>) -> Self

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

pub fn set_auto_ml_job_name(self, input: Option<String>) -> Self

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

pub fn get_auto_ml_job_name(&self) -> &Option<String>

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

pub fn auto_ml_job_input_data_config(self, input: AutoMlJobChannel) -> Self

Appends an item to AutoMLJobInputDataConfig.

To override the contents of this collection use set_auto_ml_job_input_data_config.

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.

pub fn set_auto_ml_job_input_data_config( self, input: Option<Vec<AutoMlJobChannel>>, ) -> Self

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.

pub fn get_auto_ml_job_input_data_config( &self, ) -> &Option<Vec<AutoMlJobChannel>>

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.

pub fn output_data_config(self, input: AutoMlOutputDataConfig) -> Self

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

pub fn set_output_data_config( self, input: Option<AutoMlOutputDataConfig>, ) -> Self

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

pub fn get_output_data_config(&self) -> &Option<AutoMlOutputDataConfig>

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

pub fn auto_ml_problem_type_config(self, input: AutoMlProblemTypeConfig) -> Self

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

pub fn set_auto_ml_problem_type_config( self, input: Option<AutoMlProblemTypeConfig>, ) -> Self

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

pub fn get_auto_ml_problem_type_config( &self, ) -> &Option<AutoMlProblemTypeConfig>

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

pub fn role_arn(self, input: impl Into<String>) -> Self

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

pub fn set_role_arn(self, input: Option<String>) -> Self

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

pub fn get_role_arn(&self) -> &Option<String>

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

pub fn tags(self, input: Tag) -> Self

Appends an item to Tags.

To override the contents of this collection use set_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.

pub fn set_tags(self, input: Option<Vec<Tag>>) -> Self

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.

pub fn get_tags(&self) -> &Option<Vec<Tag>>

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.

pub fn security_config(self, input: AutoMlSecurityConfig) -> Self

The security configuration for traffic encryption or Amazon VPC settings.

pub fn set_security_config(self, input: Option<AutoMlSecurityConfig>) -> Self

The security configuration for traffic encryption or Amazon VPC settings.

pub fn get_security_config(&self) -> &Option<AutoMlSecurityConfig>

The security configuration for traffic encryption or Amazon VPC settings.

pub fn auto_ml_job_objective(self, input: AutoMlJobObjective) -> Self

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.

pub fn set_auto_ml_job_objective( self, input: Option<AutoMlJobObjective>, ) -> Self

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.

pub fn get_auto_ml_job_objective(&self) -> &Option<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.

pub fn model_deploy_config(self, input: ModelDeployConfig) -> Self

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

pub fn set_model_deploy_config(self, input: Option<ModelDeployConfig>) -> Self

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

pub fn get_model_deploy_config(&self) -> &Option<ModelDeployConfig>

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

pub fn data_split_config(self, input: AutoMlDataSplitConfig) -> Self

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.

pub fn set_data_split_config(self, input: Option<AutoMlDataSplitConfig>) -> Self

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.

pub fn get_data_split_config(&self) -> &Option<AutoMlDataSplitConfig>

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.

pub fn auto_ml_compute_config(self, input: AutoMlComputeConfig) -> Self

Specifies the compute configuration for the AutoML job V2.

pub fn set_auto_ml_compute_config( self, input: Option<AutoMlComputeConfig>, ) -> Self

Specifies the compute configuration for the AutoML job V2.

pub fn get_auto_ml_compute_config(&self) -> &Option<AutoMlComputeConfig>

Specifies the compute configuration for the AutoML job V2.

Trait Implementations

impl Clone for CreateAutoMLJobV2FluentBuilder

fn clone(&self) -> CreateAutoMLJobV2FluentBuilder

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for CreateAutoMLJobV2FluentBuilder

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.