Struct aws_sdk_sagemaker::client::fluent_builders::CreateAutoMLJob

pub struct CreateAutoMLJob { /* private fields */ }

Fluent builder constructing a request to CreateAutoMLJob.

Creates an Autopilot job.

Find the best-performing model after you run an Autopilot job by calling .

For information about how to use Autopilot, see Automate Model Development with Amazon SageMaker Autopilot.

Implementations

impl CreateAutoMLJob

pub async fn customize(
    self
) -> Result<CustomizableOperation<CreateAutoMLJob, AwsResponseRetryClassifier>, SdkError<CreateAutoMLJobError>>

Consume this builder, creating a customizable operation that can be modified before being sent. The operation’s inner http::Request can be modified as well.

pub async fn send(
    self
) -> Result<CreateAutoMlJobOutput, SdkError<CreateAutoMLJobError>>

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 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 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 . 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 . 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 problem_type(self, input: ProblemType) -> Self

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

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

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

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

Defines the objective metric used to measure the predictive quality of an AutoML job. You provide an AutoMLJobObjective$MetricName and Autopilot infers whether to minimize or maximize it.

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

Defines the objective metric used to measure the predictive quality of an AutoML job. You provide an AutoMLJobObjective$MetricName and Autopilot infers whether to minimize or maximize it.

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 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 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 tags(self, input: Tag) -> Self

Appends an item to Tags.

To override the contents of this collection use set_tags.

Each tag consists of a key and an optional value. Tag keys must be unique per resource.

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

Each tag consists of a key and an optional value. 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.

Trait Implementations

impl Clone for CreateAutoMLJob

fn clone(&self) -> CreateAutoMLJob

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for CreateAutoMLJob

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

Formats the value using the given formatter.