Struct aws_sdk_sagemaker::types::builders::AutoMlJobConfigBuilder

#[non_exhaustive]
pub struct AutoMlJobConfigBuilder { /* private fields */ }

A builder for AutoMlJobConfig.

Implementations

impl AutoMlJobConfigBuilder

pub fn completion_criteria(self, input: AutoMlJobCompletionCriteria) -> Self

How long an AutoML job is allowed to run, or how many candidates a job is allowed to generate.

pub fn set_completion_criteria( self, input: Option<AutoMlJobCompletionCriteria>, ) -> Self

How long an AutoML job is allowed to run, or how many candidates a job is allowed to generate.

pub fn get_completion_criteria(&self) -> &Option<AutoMlJobCompletionCriteria>

How long an AutoML job is allowed to run, or how many candidates a job is allowed to generate.

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 candidate_generation_config( self, input: AutoMlCandidateGenerationConfig, ) -> Self

The configuration for generating a candidate for an AutoML job (optional).

pub fn set_candidate_generation_config( self, input: Option<AutoMlCandidateGenerationConfig>, ) -> Self

The configuration for generating a candidate for an AutoML job (optional).

pub fn get_candidate_generation_config( &self, ) -> &Option<AutoMlCandidateGenerationConfig>

The configuration for generating a candidate for an AutoML job (optional).

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

The configuration for splitting the input training dataset.

Type: AutoMLDataSplitConfig

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

The configuration for splitting the input training dataset.

Type: AutoMLDataSplitConfig

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

The configuration for splitting the input training dataset.

Type: AutoMLDataSplitConfig

pub fn mode(self, input: AutoMlMode) -> Self

The method that Autopilot uses to train the data. You can either specify the mode manually or let Autopilot choose for you based on the dataset size by selecting AUTO. In AUTO mode, Autopilot chooses ENSEMBLING for datasets smaller than 100 MB, and HYPERPARAMETER_TUNING for larger ones.

The ENSEMBLING mode uses a multi-stack ensemble model to predict classification and regression tasks directly from your dataset. This machine learning mode combines several base models to produce an optimal predictive model. It then uses a stacking ensemble method to combine predictions from contributing members. A multi-stack ensemble model can provide better performance over a single model by combining the predictive capabilities of multiple models. See Autopilot algorithm support for a list of algorithms supported by ENSEMBLING mode.

The HYPERPARAMETER_TUNING (HPO) mode uses the best hyperparameters to train the best version of a model. HPO automatically selects an algorithm for the type of problem you want to solve. Then HPO finds the best hyperparameters according to your objective metric. See Autopilot algorithm support for a list of algorithms supported by HYPERPARAMETER_TUNING mode.

pub fn set_mode(self, input: Option<AutoMlMode>) -> Self

The method that Autopilot uses to train the data. You can either specify the mode manually or let Autopilot choose for you based on the dataset size by selecting AUTO. In AUTO mode, Autopilot chooses ENSEMBLING for datasets smaller than 100 MB, and HYPERPARAMETER_TUNING for larger ones.

The ENSEMBLING mode uses a multi-stack ensemble model to predict classification and regression tasks directly from your dataset. This machine learning mode combines several base models to produce an optimal predictive model. It then uses a stacking ensemble method to combine predictions from contributing members. A multi-stack ensemble model can provide better performance over a single model by combining the predictive capabilities of multiple models. See Autopilot algorithm support for a list of algorithms supported by ENSEMBLING mode.

The HYPERPARAMETER_TUNING (HPO) mode uses the best hyperparameters to train the best version of a model. HPO automatically selects an algorithm for the type of problem you want to solve. Then HPO finds the best hyperparameters according to your objective metric. See Autopilot algorithm support for a list of algorithms supported by HYPERPARAMETER_TUNING mode.

pub fn get_mode(&self) -> &Option<AutoMlMode>

The method that Autopilot uses to train the data. You can either specify the mode manually or let Autopilot choose for you based on the dataset size by selecting AUTO. In AUTO mode, Autopilot chooses ENSEMBLING for datasets smaller than 100 MB, and HYPERPARAMETER_TUNING for larger ones.

The ENSEMBLING mode uses a multi-stack ensemble model to predict classification and regression tasks directly from your dataset. This machine learning mode combines several base models to produce an optimal predictive model. It then uses a stacking ensemble method to combine predictions from contributing members. A multi-stack ensemble model can provide better performance over a single model by combining the predictive capabilities of multiple models. See Autopilot algorithm support for a list of algorithms supported by ENSEMBLING mode.

The HYPERPARAMETER_TUNING (HPO) mode uses the best hyperparameters to train the best version of a model. HPO automatically selects an algorithm for the type of problem you want to solve. Then HPO finds the best hyperparameters according to your objective metric. See Autopilot algorithm support for a list of algorithms supported by HYPERPARAMETER_TUNING mode.

pub fn build(self) -> AutoMlJobConfig

Consumes the builder and constructs a AutoMlJobConfig.

Trait Implementations

impl Clone for AutoMlJobConfigBuilder

fn clone(&self) -> AutoMlJobConfigBuilder

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for AutoMlJobConfigBuilder

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

Formats the value using the given formatter.

impl Default for AutoMlJobConfigBuilder

fn default() -> AutoMlJobConfigBuilder

Returns the “default value” for a type.

impl PartialEq for AutoMlJobConfigBuilder

fn eq(&self, other: &AutoMlJobConfigBuilder) -> bool

Tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

Tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl StructuralPartialEq for AutoMlJobConfigBuilder