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

pub struct CreateSolutionFluentBuilder { /* private fields */ }

Fluent builder constructing a request to CreateSolution.

Creates the configuration for training a model. A trained model is known as a solution. After the configuration is created, you train the model (create a solution) by calling the CreateSolutionVersion operation. Every time you call CreateSolutionVersion, a new version of the solution is created.

After creating a solution version, you check its accuracy by calling GetSolutionMetrics. When you are satisfied with the version, you deploy it using CreateCampaign. The campaign provides recommendations to a client through the GetRecommendations API.

To train a model, Amazon Personalize requires training data and a recipe. The training data comes from the dataset group that you provide in the request. A recipe specifies the training algorithm and a feature transformation. You can specify one of the predefined recipes provided by Amazon Personalize. Alternatively, you can specify performAutoML and Amazon Personalize will analyze your data and select the optimum USER_PERSONALIZATION recipe for you.

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. Wait until the status shows as ACTIVE before calling CreateSolutionVersion.

Related APIs

• ListSolutions

• CreateSolutionVersion

• DescribeSolution

• DeleteSolution

• ListSolutionVersions

• DescribeSolutionVersion

Implementations

impl CreateSolutionFluentBuilder

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

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 async fn customize( self ) -> Result<CustomizableOperation<CreateSolution, AwsResponseRetryClassifier>, SdkError<CreateSolutionError>>

Consumes 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 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 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 perform_auto_ml(self, input: bool) -> Self

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

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

The ARN of the recipe to use for model training. Only specified when performAutoML is false.

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

The ARN of the recipe to use for model training. Only specified when performAutoML is false.

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 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 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 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.

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.