Struct CreateAutoMLJobFluentBuilder 

pub struct CreateAutoMLJobFluentBuilder { /* private fields */ }

Fluent builder constructing a request to CreateAutoMLJob.

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

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.

We recommend using the new versions CreateAutoMLJobV2 and DescribeAutoMLJobV2, 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.

You can find the best-performing model after you run an AutoML job by calling DescribeAutoMLJobV2 (recommended) or DescribeAutoMLJob.

Implementations

impl CreateAutoMLJobFluentBuilder

pub fn as_input(&self) -> &CreateAutoMlJobInputBuilder

Access the CreateAutoMLJob as a reference.

pub async fn send( self, ) -> Result<CreateAutoMlJobOutput, SdkError<CreateAutoMLJobError, 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<CreateAutoMlJobOutput, CreateAutoMLJobError, 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 input_data_config(self, input: AutoMlChannel) -> Self

Appends an item to InputDataConfig.

To override the contents of this collection use set_input_data_config.

An array of channel objects that describes the input data and its location. Each channel is a named input source. Similar to InputDataConfig supported by HyperParameterTrainingJobDefinition. Format(s) supported: CSV, Parquet. A minimum of 500 rows is required for the training dataset. There is not a minimum number of rows required for the validation dataset.

pub fn set_input_data_config(self, input: Option<Vec<AutoMlChannel>>) -> Self

An array of channel objects that describes the input data and its location. Each channel is a named input source. Similar to InputDataConfig supported by HyperParameterTrainingJobDefinition. Format(s) supported: CSV, Parquet. A minimum of 500 rows is required for the training dataset. There is not a minimum number of rows required for the validation dataset.

pub fn get_input_data_config(&self) -> &Option<Vec<AutoMlChannel>>

An array of channel objects that describes the input data and its location. Each channel is a named input source. Similar to InputDataConfig supported by HyperParameterTrainingJobDefinition. Format(s) supported: CSV, Parquet. A minimum of 500 rows is required for the training dataset. There is not a minimum number of rows required for the validation dataset.

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. Format(s) supported: CSV.

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. Format(s) supported: CSV.

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. Format(s) supported: CSV.

pub fn problem_type(self, input: ProblemType) -> Self

Defines the type of supervised learning problem available for the candidates. For more information, see SageMaker Autopilot problem types.

pub fn set_problem_type(self, input: Option<ProblemType>) -> Self

Defines the type of supervised learning problem available for the candidates. For more information, see SageMaker Autopilot problem types.

pub fn get_problem_type(&self) -> &Option<ProblemType>

Defines the type of supervised learning problem available for the candidates. For more information, see SageMaker Autopilot problem types.

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. See AutoMLJobObjective for the default values.

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. See AutoMLJobObjective for the default values.

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. See AutoMLJobObjective for the default values.

pub fn auto_ml_job_config(self, input: AutoMlJobConfig) -> Self

A collection of settings used to configure an AutoML job.

pub fn set_auto_ml_job_config(self, input: Option<AutoMlJobConfig>) -> Self

A collection of settings used to configure an AutoML job.

pub fn get_auto_ml_job_config(&self) -> &Option<AutoMlJobConfig>

A collection of settings used to configure an AutoML job.

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 generate_candidate_definitions_only(self, input: bool) -> Self

Generates possible candidates without training the models. A candidate is a combination of data preprocessors, algorithms, and algorithm parameter settings.

pub fn set_generate_candidate_definitions_only( self, input: Option<bool>, ) -> Self

Generates possible candidates without training the models. A candidate is a combination of data preprocessors, algorithms, and algorithm parameter settings.

pub fn get_generate_candidate_definitions_only(&self) -> &Option<bool>

Generates possible candidates without training the models. A candidate is a combination of data preprocessors, algorithms, and algorithm parameter settings.

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, for example, 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, for example, 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, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web ServicesResources. Tag keys must be unique per resource.

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.

Trait Implementations

impl Clone for CreateAutoMLJobFluentBuilder

fn clone(&self) -> CreateAutoMLJobFluentBuilder

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for CreateAutoMLJobFluentBuilder

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

Formats the value using the given formatter.