Struct aws_sdk_personalize::operation::create_solution::builders::CreateSolutionFluentBuilder

pub struct CreateSolutionFluentBuilder { /* private fields */ }

Fluent builder constructing a request to CreateSolution.

After you create a solution, you can’t change its configuration. By default, all new solutions use automatic training. With automatic training, you incur training costs while your solution is active. You can't stop automatic training for a solution. To avoid unnecessary costs, make sure to delete the solution when you are finished. For information about training costs, see Amazon Personalize pricing.

Creates the configuration for training a model (creating a solution version). This configuration includes the recipe to use for model training and optional training configuration, such as columns to use in training and feature transformation parameters. For more information about configuring a solution, see Creating and configuring a solution.

By default, new solutions use automatic training to create solution versions every 7 days. You can change the training frequency. Automatic solution version creation starts one hour after the solution is ACTIVE. If you manually create a solution version within the hour, the solution skips the first automatic training. For more information, see Configuring automatic training.

To turn off automatic training, set performAutoTraining to false. If you turn off automatic training, you must manually create a solution version by calling the CreateSolutionVersion operation.

After training starts, you can get the solution version's Amazon Resource Name (ARN) with the ListSolutionVersions API operation. To get its status, use the DescribeSolutionVersion.

After training completes you can evaluate model accuracy by calling GetSolutionMetrics. When you are satisfied with the solution version, you deploy it using CreateCampaign. The campaign provides recommendations to a client through the GetRecommendations API.

Amazon Personalize doesn't support configuring the hpoObjective for solution hyperparameter optimization at this time.

Status

A solution can be in one of the following states:

• CREATE PENDING > CREATE IN_PROGRESS > ACTIVE -or- CREATE FAILED

• DELETE PENDING > DELETE IN_PROGRESS

To get the status of the solution, call DescribeSolution. If you use manual training, the status must be ACTIVE before you call CreateSolutionVersion.

Related APIs

• ListSolutions

• CreateSolutionVersion

• DescribeSolution

• DeleteSolution

• ListSolutionVersions

• DescribeSolutionVersion

Implementations

impl CreateSolutionFluentBuilder

pub fn as_input(&self) -> &CreateSolutionInputBuilder

Access the CreateSolution as a reference.

pub async fn send( self ) -> Result<CreateSolutionOutput, SdkError<CreateSolutionError, 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<CreateSolutionOutput, CreateSolutionError, Self>

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

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

The name for the solution.

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

The name for the solution.

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

The name for the solution.

pub fn perform_hpo(self, input: bool) -> Self

Whether to perform hyperparameter optimization (HPO) on the specified or selected recipe. The default is false.

When performing AutoML, this parameter is always true and you should not set it to false.

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

Whether to perform hyperparameter optimization (HPO) on the specified or selected recipe. The default is false.

When performing AutoML, this parameter is always true and you should not set it to false.

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

Whether to perform hyperparameter optimization (HPO) on the specified or selected recipe. The default is false.

When performing AutoML, this parameter is always true and you should not set it to false.

pub fn perform_auto_ml(self, input: bool) -> Self

We don't recommend enabling automated machine learning. Instead, match your use case to the available Amazon Personalize recipes. For more information, see Choosing a recipe.

Whether to perform automated machine learning (AutoML). The default is false. For this case, you must specify recipeArn.

When set to true, Amazon Personalize analyzes your training data and selects the optimal USER_PERSONALIZATION recipe and hyperparameters. In this case, you must omit recipeArn. Amazon Personalize determines the optimal recipe by running tests with different values for the hyperparameters. AutoML lengthens the training process as compared to selecting a specific recipe.

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

We don't recommend enabling automated machine learning. Instead, match your use case to the available Amazon Personalize recipes. For more information, see Choosing a recipe.

Whether to perform automated machine learning (AutoML). The default is false. For this case, you must specify recipeArn.

When set to true, Amazon Personalize analyzes your training data and selects the optimal USER_PERSONALIZATION recipe and hyperparameters. In this case, you must omit recipeArn. Amazon Personalize determines the optimal recipe by running tests with different values for the hyperparameters. AutoML lengthens the training process as compared to selecting a specific recipe.

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

We don't recommend enabling automated machine learning. Instead, match your use case to the available Amazon Personalize recipes. For more information, see Choosing a recipe.

Whether to perform automated machine learning (AutoML). The default is false. For this case, you must specify recipeArn.

When set to true, Amazon Personalize analyzes your training data and selects the optimal USER_PERSONALIZATION recipe and hyperparameters. In this case, you must omit recipeArn. Amazon Personalize determines the optimal recipe by running tests with different values for the hyperparameters. AutoML lengthens the training process as compared to selecting a specific recipe.

pub fn perform_auto_training(self, input: bool) -> Self

Whether the solution uses automatic training to create new solution versions (trained models). The default is True and the solution automatically creates new solution versions every 7 days. You can change the training frequency by specifying a schedulingExpression in the AutoTrainingConfig as part of solution configuration. For more information about automatic training, see Configuring automatic training.

Automatic solution version creation starts one hour after the solution is ACTIVE. If you manually create a solution version within the hour, the solution skips the first automatic training.

After training starts, you can get the solution version's Amazon Resource Name (ARN) with the ListSolutionVersions API operation. To get its status, use the DescribeSolutionVersion.

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

Whether the solution uses automatic training to create new solution versions (trained models). The default is True and the solution automatically creates new solution versions every 7 days. You can change the training frequency by specifying a schedulingExpression in the AutoTrainingConfig as part of solution configuration. For more information about automatic training, see Configuring automatic training.

Automatic solution version creation starts one hour after the solution is ACTIVE. If you manually create a solution version within the hour, the solution skips the first automatic training.

After training starts, you can get the solution version's Amazon Resource Name (ARN) with the ListSolutionVersions API operation. To get its status, use the DescribeSolutionVersion.

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

Whether the solution uses automatic training to create new solution versions (trained models). The default is True and the solution automatically creates new solution versions every 7 days. You can change the training frequency by specifying a schedulingExpression in the AutoTrainingConfig as part of solution configuration. For more information about automatic training, see Configuring automatic training.

Automatic solution version creation starts one hour after the solution is ACTIVE. If you manually create a solution version within the hour, the solution skips the first automatic training.

After training starts, you can get the solution version's Amazon Resource Name (ARN) with the ListSolutionVersions API operation. To get its status, use the DescribeSolutionVersion.

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

The Amazon Resource Name (ARN) of the recipe to use for model training. This is required when performAutoML is false. For information about different Amazon Personalize recipes and their ARNs, see Choosing a recipe.

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

The Amazon Resource Name (ARN) of the recipe to use for model training. This is required when performAutoML is false. For information about different Amazon Personalize recipes and their ARNs, see Choosing a recipe.

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

The Amazon Resource Name (ARN) of the recipe to use for model training. This is required when performAutoML is false. For information about different Amazon Personalize recipes and their ARNs, see Choosing a recipe.

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

The Amazon Resource Name (ARN) of the dataset group that provides the training data.

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

The Amazon Resource Name (ARN) of the dataset group that provides the training data.

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

The Amazon Resource Name (ARN) of the dataset group that provides the training data.

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

When your have multiple event types (using an EVENT_TYPE schema field), this parameter specifies which event type (for example, 'click' or 'like') is used for training the model.

If you do not provide an eventType, Amazon Personalize will use all interactions for training with equal weight regardless of type.

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

When your have multiple event types (using an EVENT_TYPE schema field), this parameter specifies which event type (for example, 'click' or 'like') is used for training the model.

If you do not provide an eventType, Amazon Personalize will use all interactions for training with equal weight regardless of type.

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

When your have multiple event types (using an EVENT_TYPE schema field), this parameter specifies which event type (for example, 'click' or 'like') is used for training the model.

If you do not provide an eventType, Amazon Personalize will use all interactions for training with equal weight regardless of type.

pub fn solution_config(self, input: SolutionConfig) -> Self

The configuration to use with the solution. When performAutoML is set to true, Amazon Personalize only evaluates the autoMLConfig section of the solution configuration.

Amazon Personalize doesn't support configuring the hpoObjective at this time.

pub fn set_solution_config(self, input: Option<SolutionConfig>) -> Self

The configuration to use with the solution. When performAutoML is set to true, Amazon Personalize only evaluates the autoMLConfig section of the solution configuration.

Amazon Personalize doesn't support configuring the hpoObjective at this time.

pub fn get_solution_config(&self) -> &Option<SolutionConfig>

The configuration to use with the solution. When performAutoML is set to true, Amazon Personalize only evaluates the autoMLConfig section of the solution configuration.

Amazon Personalize doesn't support configuring the hpoObjective at this time.

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

Appends an item to tags.

To override the contents of this collection use set_tags.

A list of tags to apply to the solution.

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

A list of tags to apply to the solution.

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

A list of tags to apply to the solution.

Trait Implementations

impl Clone for CreateSolutionFluentBuilder

fn clone(&self) -> CreateSolutionFluentBuilder

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for CreateSolutionFluentBuilder

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

Formats the value using the given formatter.