aws-sdk-personalize 1.105.0

// Code generated by software.amazon.smithy.rust.codegen.smithy-rs. DO NOT EDIT.

/// <important>
/// <p>By default, all new solutions use automatic training. With automatic training, you incur training costs while your solution is active. To avoid unnecessary costs, when you are finished you can <a href="https://docs.aws.amazon.com/personalize/latest/dg/API_UpdateSolution.html">update the solution</a> to turn off automatic training. For information about training costs, see <a href="https://aws.amazon.com/personalize/pricing/">Amazon Personalize pricing</a>.</p>
/// </important>
/// <p>An object that provides information about a solution. A solution includes the custom recipe, customized parameters, and trained models (Solution Versions) that Amazon Personalize uses to generate recommendations.</p>
/// <p>After you create a solution, you can’t change its configuration. If you need to make changes, you can <a href="https://docs.aws.amazon.com/personalize/latest/dg/cloning-solution.html">clone the solution</a> with the Amazon Personalize console or create a new one.</p>
#[non_exhaustive]
#[derive(::std::clone::Clone, ::std::cmp::PartialEq, ::std::fmt::Debug)]
pub struct Solution {
    /// <p>The name of the solution.</p>
    pub name: ::std::option::Option<::std::string::String>,
    /// <p>The ARN of the solution.</p>
    pub solution_arn: ::std::option::Option<::std::string::String>,
    /// <p>Whether to perform hyperparameter optimization (HPO) on the chosen recipe. The default is <code>false</code>.</p>
    pub perform_hpo: bool,
    /// <important>
    /// <p>We don't recommend enabling automated machine learning. Instead, match your use case to the available Amazon Personalize recipes. For more information, see <a href="https://docs.aws.amazon.com/personalize/latest/dg/determining-use-case.html">Determining your use case.</a></p>
    /// </important>
    /// <p>When true, Amazon Personalize performs a search for the best USER_PERSONALIZATION recipe from the list specified in the solution configuration (<code>recipeArn</code> must not be specified). When false (the default), Amazon Personalize uses <code>recipeArn</code> for training.</p>
    pub perform_auto_ml: bool,
    /// <p>Specifies whether the solution automatically creates solution versions. The default is <code>True</code> and the solution automatically creates new solution versions every 7 days.</p>
    /// <p>For more information about auto training, see <a href="https://docs.aws.amazon.com/personalize/latest/dg/customizing-solution-config.html">Creating and configuring a solution</a>.</p>
    pub perform_auto_training: ::std::option::Option<bool>,
    /// <p>A Boolean value that indicates whether incremental training updates are performed on the model. When enabled, this allows the model to learn from new data more frequently without requiring full retraining, which enables near real-time personalization. This parameter is supported only for solutions that use the semantic-similarity recipe</p>
    pub perform_incremental_update: ::std::option::Option<bool>,
    /// <p>The ARN of the recipe used to create the solution. This is required when <code>performAutoML</code> is false.</p>
    pub recipe_arn: ::std::option::Option<::std::string::String>,
    /// <p>The Amazon Resource Name (ARN) of the dataset group that provides the training data.</p>
    pub dataset_group_arn: ::std::option::Option<::std::string::String>,
    /// <p>The event type (for example, 'click' or 'like') that is used for training the model. If no <code>eventType</code> is provided, Amazon Personalize uses all interactions for training with equal weight regardless of type.</p>
    pub event_type: ::std::option::Option<::std::string::String>,
    /// <p>Describes the configuration properties for the solution.</p>
    pub solution_config: ::std::option::Option<crate::types::SolutionConfig>,
    /// <p>When <code>performAutoML</code> is true, specifies the best recipe found.</p>
    pub auto_ml_result: ::std::option::Option<crate::types::AutoMlResult>,
    /// <p>The status of the solution.</p>
    /// <p>A solution can be in one of the following states:</p>
    /// <ul>
    /// <li>
    /// <p>CREATE PENDING &gt; CREATE IN_PROGRESS &gt; ACTIVE -or- CREATE FAILED</p></li>
    /// <li>
    /// <p>DELETE PENDING &gt; DELETE IN_PROGRESS</p></li>
    /// </ul>
    pub status: ::std::option::Option<::std::string::String>,
    /// <p>The creation date and time (in Unix time) of the solution.</p>
    pub creation_date_time: ::std::option::Option<::aws_smithy_types::DateTime>,
    /// <p>The date and time (in Unix time) that the solution was last updated.</p>
    pub last_updated_date_time: ::std::option::Option<::aws_smithy_types::DateTime>,
    /// <p>Describes the latest version of the solution, including the status and the ARN.</p>
    pub latest_solution_version: ::std::option::Option<crate::types::SolutionVersionSummary>,
    /// <p>Provides a summary of the latest updates to the solution.</p>
    pub latest_solution_update: ::std::option::Option<crate::types::SolutionUpdateSummary>,
}
impl Solution {
    /// <p>The name of the solution.</p>
    pub fn name(&self) -> ::std::option::Option<&str> {
        self.name.as_deref()
    }
    /// <p>The ARN of the solution.</p>
    pub fn solution_arn(&self) -> ::std::option::Option<&str> {
        self.solution_arn.as_deref()
    }
    /// <p>Whether to perform hyperparameter optimization (HPO) on the chosen recipe. The default is <code>false</code>.</p>
    pub fn perform_hpo(&self) -> bool {
        self.perform_hpo
    }
    /// <important>
    /// <p>We don't recommend enabling automated machine learning. Instead, match your use case to the available Amazon Personalize recipes. For more information, see <a href="https://docs.aws.amazon.com/personalize/latest/dg/determining-use-case.html">Determining your use case.</a></p>
    /// </important>
    /// <p>When true, Amazon Personalize performs a search for the best USER_PERSONALIZATION recipe from the list specified in the solution configuration (<code>recipeArn</code> must not be specified). When false (the default), Amazon Personalize uses <code>recipeArn</code> for training.</p>
    pub fn perform_auto_ml(&self) -> bool {
        self.perform_auto_ml
    }
    /// <p>Specifies whether the solution automatically creates solution versions. The default is <code>True</code> and the solution automatically creates new solution versions every 7 days.</p>
    /// <p>For more information about auto training, see <a href="https://docs.aws.amazon.com/personalize/latest/dg/customizing-solution-config.html">Creating and configuring a solution</a>.</p>
    pub fn perform_auto_training(&self) -> ::std::option::Option<bool> {
        self.perform_auto_training
    }
    /// <p>A Boolean value that indicates whether incremental training updates are performed on the model. When enabled, this allows the model to learn from new data more frequently without requiring full retraining, which enables near real-time personalization. This parameter is supported only for solutions that use the semantic-similarity recipe</p>
    pub fn perform_incremental_update(&self) -> ::std::option::Option<bool> {
        self.perform_incremental_update
    }
    /// <p>The ARN of the recipe used to create the solution. This is required when <code>performAutoML</code> is false.</p>
    pub fn recipe_arn(&self) -> ::std::option::Option<&str> {
        self.recipe_arn.as_deref()
    }
    /// <p>The Amazon Resource Name (ARN) of the dataset group that provides the training data.</p>
    pub fn dataset_group_arn(&self) -> ::std::option::Option<&str> {
        self.dataset_group_arn.as_deref()
    }
    /// <p>The event type (for example, 'click' or 'like') that is used for training the model. If no <code>eventType</code> is provided, Amazon Personalize uses all interactions for training with equal weight regardless of type.</p>
    pub fn event_type(&self) -> ::std::option::Option<&str> {
        self.event_type.as_deref()
    }
    /// <p>Describes the configuration properties for the solution.</p>
    pub fn solution_config(&self) -> ::std::option::Option<&crate::types::SolutionConfig> {
        self.solution_config.as_ref()
    }
    /// <p>When <code>performAutoML</code> is true, specifies the best recipe found.</p>
    pub fn auto_ml_result(&self) -> ::std::option::Option<&crate::types::AutoMlResult> {
        self.auto_ml_result.as_ref()
    }
    /// <p>The status of the solution.</p>
    /// <p>A solution can be in one of the following states:</p>
    /// <ul>
    /// <li>
    /// <p>CREATE PENDING &gt; CREATE IN_PROGRESS &gt; ACTIVE -or- CREATE FAILED</p></li>
    /// <li>
    /// <p>DELETE PENDING &gt; DELETE IN_PROGRESS</p></li>
    /// </ul>
    pub fn status(&self) -> ::std::option::Option<&str> {
        self.status.as_deref()
    }
    /// <p>The creation date and time (in Unix time) of the solution.</p>
    pub fn creation_date_time(&self) -> ::std::option::Option<&::aws_smithy_types::DateTime> {
        self.creation_date_time.as_ref()
    }
    /// <p>The date and time (in Unix time) that the solution was last updated.</p>
    pub fn last_updated_date_time(&self) -> ::std::option::Option<&::aws_smithy_types::DateTime> {
        self.last_updated_date_time.as_ref()
    }
    /// <p>Describes the latest version of the solution, including the status and the ARN.</p>
    pub fn latest_solution_version(&self) -> ::std::option::Option<&crate::types::SolutionVersionSummary> {
        self.latest_solution_version.as_ref()
    }
    /// <p>Provides a summary of the latest updates to the solution.</p>
    pub fn latest_solution_update(&self) -> ::std::option::Option<&crate::types::SolutionUpdateSummary> {
        self.latest_solution_update.as_ref()
    }
}
impl Solution {
    /// Creates a new builder-style object to manufacture [`Solution`](crate::types::Solution).
    pub fn builder() -> crate::types::builders::SolutionBuilder {
        crate::types::builders::SolutionBuilder::default()
    }
}

/// A builder for [`Solution`](crate::types::Solution).
#[derive(::std::clone::Clone, ::std::cmp::PartialEq, ::std::default::Default, ::std::fmt::Debug)]
#[non_exhaustive]
pub struct SolutionBuilder {
    pub(crate) name: ::std::option::Option<::std::string::String>,
    pub(crate) solution_arn: ::std::option::Option<::std::string::String>,
    pub(crate) perform_hpo: ::std::option::Option<bool>,
    pub(crate) perform_auto_ml: ::std::option::Option<bool>,
    pub(crate) perform_auto_training: ::std::option::Option<bool>,
    pub(crate) perform_incremental_update: ::std::option::Option<bool>,
    pub(crate) recipe_arn: ::std::option::Option<::std::string::String>,
    pub(crate) dataset_group_arn: ::std::option::Option<::std::string::String>,
    pub(crate) event_type: ::std::option::Option<::std::string::String>,
    pub(crate) solution_config: ::std::option::Option<crate::types::SolutionConfig>,
    pub(crate) auto_ml_result: ::std::option::Option<crate::types::AutoMlResult>,
    pub(crate) status: ::std::option::Option<::std::string::String>,
    pub(crate) creation_date_time: ::std::option::Option<::aws_smithy_types::DateTime>,
    pub(crate) last_updated_date_time: ::std::option::Option<::aws_smithy_types::DateTime>,
    pub(crate) latest_solution_version: ::std::option::Option<crate::types::SolutionVersionSummary>,
    pub(crate) latest_solution_update: ::std::option::Option<crate::types::SolutionUpdateSummary>,
}
impl SolutionBuilder {
    /// <p>The name of the solution.</p>
    pub fn name(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {
        self.name = ::std::option::Option::Some(input.into());
        self
    }
    /// <p>The name of the solution.</p>
    pub fn set_name(mut self, input: ::std::option::Option<::std::string::String>) -> Self {
        self.name = input;
        self
    }
    /// <p>The name of the solution.</p>
    pub fn get_name(&self) -> &::std::option::Option<::std::string::String> {
        &self.name
    }
    /// <p>The ARN of the solution.</p>
    pub fn solution_arn(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {
        self.solution_arn = ::std::option::Option::Some(input.into());
        self
    }
    /// <p>The ARN of the solution.</p>
    pub fn set_solution_arn(mut self, input: ::std::option::Option<::std::string::String>) -> Self {
        self.solution_arn = input;
        self
    }
    /// <p>The ARN of the solution.</p>
    pub fn get_solution_arn(&self) -> &::std::option::Option<::std::string::String> {
        &self.solution_arn
    }
    /// <p>Whether to perform hyperparameter optimization (HPO) on the chosen recipe. The default is <code>false</code>.</p>
    pub fn perform_hpo(mut self, input: bool) -> Self {
        self.perform_hpo = ::std::option::Option::Some(input);
        self
    }
    /// <p>Whether to perform hyperparameter optimization (HPO) on the chosen recipe. The default is <code>false</code>.</p>
    pub fn set_perform_hpo(mut self, input: ::std::option::Option<bool>) -> Self {
        self.perform_hpo = input;
        self
    }
    /// <p>Whether to perform hyperparameter optimization (HPO) on the chosen recipe. The default is <code>false</code>.</p>
    pub fn get_perform_hpo(&self) -> &::std::option::Option<bool> {
        &self.perform_hpo
    }
    /// <important>
    /// <p>We don't recommend enabling automated machine learning. Instead, match your use case to the available Amazon Personalize recipes. For more information, see <a href="https://docs.aws.amazon.com/personalize/latest/dg/determining-use-case.html">Determining your use case.</a></p>
    /// </important>
    /// <p>When true, Amazon Personalize performs a search for the best USER_PERSONALIZATION recipe from the list specified in the solution configuration (<code>recipeArn</code> must not be specified). When false (the default), Amazon Personalize uses <code>recipeArn</code> for training.</p>
    pub fn perform_auto_ml(mut self, input: bool) -> Self {
        self.perform_auto_ml = ::std::option::Option::Some(input);
        self
    }
    /// <important>
    /// <p>We don't recommend enabling automated machine learning. Instead, match your use case to the available Amazon Personalize recipes. For more information, see <a href="https://docs.aws.amazon.com/personalize/latest/dg/determining-use-case.html">Determining your use case.</a></p>
    /// </important>
    /// <p>When true, Amazon Personalize performs a search for the best USER_PERSONALIZATION recipe from the list specified in the solution configuration (<code>recipeArn</code> must not be specified). When false (the default), Amazon Personalize uses <code>recipeArn</code> for training.</p>
    pub fn set_perform_auto_ml(mut self, input: ::std::option::Option<bool>) -> Self {
        self.perform_auto_ml = input;
        self
    }
    /// <important>
    /// <p>We don't recommend enabling automated machine learning. Instead, match your use case to the available Amazon Personalize recipes. For more information, see <a href="https://docs.aws.amazon.com/personalize/latest/dg/determining-use-case.html">Determining your use case.</a></p>
    /// </important>
    /// <p>When true, Amazon Personalize performs a search for the best USER_PERSONALIZATION recipe from the list specified in the solution configuration (<code>recipeArn</code> must not be specified). When false (the default), Amazon Personalize uses <code>recipeArn</code> for training.</p>
    pub fn get_perform_auto_ml(&self) -> &::std::option::Option<bool> {
        &self.perform_auto_ml
    }
    /// <p>Specifies whether the solution automatically creates solution versions. The default is <code>True</code> and the solution automatically creates new solution versions every 7 days.</p>
    /// <p>For more information about auto training, see <a href="https://docs.aws.amazon.com/personalize/latest/dg/customizing-solution-config.html">Creating and configuring a solution</a>.</p>
    pub fn perform_auto_training(mut self, input: bool) -> Self {
        self.perform_auto_training = ::std::option::Option::Some(input);
        self
    }
    /// <p>Specifies whether the solution automatically creates solution versions. The default is <code>True</code> and the solution automatically creates new solution versions every 7 days.</p>
    /// <p>For more information about auto training, see <a href="https://docs.aws.amazon.com/personalize/latest/dg/customizing-solution-config.html">Creating and configuring a solution</a>.</p>
    pub fn set_perform_auto_training(mut self, input: ::std::option::Option<bool>) -> Self {
        self.perform_auto_training = input;
        self
    }
    /// <p>Specifies whether the solution automatically creates solution versions. The default is <code>True</code> and the solution automatically creates new solution versions every 7 days.</p>
    /// <p>For more information about auto training, see <a href="https://docs.aws.amazon.com/personalize/latest/dg/customizing-solution-config.html">Creating and configuring a solution</a>.</p>
    pub fn get_perform_auto_training(&self) -> &::std::option::Option<bool> {
        &self.perform_auto_training
    }
    /// <p>A Boolean value that indicates whether incremental training updates are performed on the model. When enabled, this allows the model to learn from new data more frequently without requiring full retraining, which enables near real-time personalization. This parameter is supported only for solutions that use the semantic-similarity recipe</p>
    pub fn perform_incremental_update(mut self, input: bool) -> Self {
        self.perform_incremental_update = ::std::option::Option::Some(input);
        self
    }
    /// <p>A Boolean value that indicates whether incremental training updates are performed on the model. When enabled, this allows the model to learn from new data more frequently without requiring full retraining, which enables near real-time personalization. This parameter is supported only for solutions that use the semantic-similarity recipe</p>
    pub fn set_perform_incremental_update(mut self, input: ::std::option::Option<bool>) -> Self {
        self.perform_incremental_update = input;
        self
    }
    /// <p>A Boolean value that indicates whether incremental training updates are performed on the model. When enabled, this allows the model to learn from new data more frequently without requiring full retraining, which enables near real-time personalization. This parameter is supported only for solutions that use the semantic-similarity recipe</p>
    pub fn get_perform_incremental_update(&self) -> &::std::option::Option<bool> {
        &self.perform_incremental_update
    }
    /// <p>The ARN of the recipe used to create the solution. This is required when <code>performAutoML</code> is false.</p>
    pub fn recipe_arn(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {
        self.recipe_arn = ::std::option::Option::Some(input.into());
        self
    }
    /// <p>The ARN of the recipe used to create the solution. This is required when <code>performAutoML</code> is false.</p>
    pub fn set_recipe_arn(mut self, input: ::std::option::Option<::std::string::String>) -> Self {
        self.recipe_arn = input;
        self
    }
    /// <p>The ARN of the recipe used to create the solution. This is required when <code>performAutoML</code> is false.</p>
    pub fn get_recipe_arn(&self) -> &::std::option::Option<::std::string::String> {
        &self.recipe_arn
    }
    /// <p>The Amazon Resource Name (ARN) of the dataset group that provides the training data.</p>
    pub fn dataset_group_arn(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {
        self.dataset_group_arn = ::std::option::Option::Some(input.into());
        self
    }
    /// <p>The Amazon Resource Name (ARN) of the dataset group that provides the training data.</p>
    pub fn set_dataset_group_arn(mut self, input: ::std::option::Option<::std::string::String>) -> Self {
        self.dataset_group_arn = input;
        self
    }
    /// <p>The Amazon Resource Name (ARN) of the dataset group that provides the training data.</p>
    pub fn get_dataset_group_arn(&self) -> &::std::option::Option<::std::string::String> {
        &self.dataset_group_arn
    }
    /// <p>The event type (for example, 'click' or 'like') that is used for training the model. If no <code>eventType</code> is provided, Amazon Personalize uses all interactions for training with equal weight regardless of type.</p>
    pub fn event_type(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {
        self.event_type = ::std::option::Option::Some(input.into());
        self
    }
    /// <p>The event type (for example, 'click' or 'like') that is used for training the model. If no <code>eventType</code> is provided, Amazon Personalize uses all interactions for training with equal weight regardless of type.</p>
    pub fn set_event_type(mut self, input: ::std::option::Option<::std::string::String>) -> Self {
        self.event_type = input;
        self
    }
    /// <p>The event type (for example, 'click' or 'like') that is used for training the model. If no <code>eventType</code> is provided, Amazon Personalize uses all interactions for training with equal weight regardless of type.</p>
    pub fn get_event_type(&self) -> &::std::option::Option<::std::string::String> {
        &self.event_type
    }
    /// <p>Describes the configuration properties for the solution.</p>
    pub fn solution_config(mut self, input: crate::types::SolutionConfig) -> Self {
        self.solution_config = ::std::option::Option::Some(input);
        self
    }
    /// <p>Describes the configuration properties for the solution.</p>
    pub fn set_solution_config(mut self, input: ::std::option::Option<crate::types::SolutionConfig>) -> Self {
        self.solution_config = input;
        self
    }
    /// <p>Describes the configuration properties for the solution.</p>
    pub fn get_solution_config(&self) -> &::std::option::Option<crate::types::SolutionConfig> {
        &self.solution_config
    }
    /// <p>When <code>performAutoML</code> is true, specifies the best recipe found.</p>
    pub fn auto_ml_result(mut self, input: crate::types::AutoMlResult) -> Self {
        self.auto_ml_result = ::std::option::Option::Some(input);
        self
    }
    /// <p>When <code>performAutoML</code> is true, specifies the best recipe found.</p>
    pub fn set_auto_ml_result(mut self, input: ::std::option::Option<crate::types::AutoMlResult>) -> Self {
        self.auto_ml_result = input;
        self
    }
    /// <p>When <code>performAutoML</code> is true, specifies the best recipe found.</p>
    pub fn get_auto_ml_result(&self) -> &::std::option::Option<crate::types::AutoMlResult> {
        &self.auto_ml_result
    }
    /// <p>The status of the solution.</p>
    /// <p>A solution can be in one of the following states:</p>
    /// <ul>
    /// <li>
    /// <p>CREATE PENDING &gt; CREATE IN_PROGRESS &gt; ACTIVE -or- CREATE FAILED</p></li>
    /// <li>
    /// <p>DELETE PENDING &gt; DELETE IN_PROGRESS</p></li>
    /// </ul>
    pub fn status(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {
        self.status = ::std::option::Option::Some(input.into());
        self
    }
    /// <p>The status of the solution.</p>
    /// <p>A solution can be in one of the following states:</p>
    /// <ul>
    /// <li>
    /// <p>CREATE PENDING &gt; CREATE IN_PROGRESS &gt; ACTIVE -or- CREATE FAILED</p></li>
    /// <li>
    /// <p>DELETE PENDING &gt; DELETE IN_PROGRESS</p></li>
    /// </ul>
    pub fn set_status(mut self, input: ::std::option::Option<::std::string::String>) -> Self {
        self.status = input;
        self
    }
    /// <p>The status of the solution.</p>
    /// <p>A solution can be in one of the following states:</p>
    /// <ul>
    /// <li>
    /// <p>CREATE PENDING &gt; CREATE IN_PROGRESS &gt; ACTIVE -or- CREATE FAILED</p></li>
    /// <li>
    /// <p>DELETE PENDING &gt; DELETE IN_PROGRESS</p></li>
    /// </ul>
    pub fn get_status(&self) -> &::std::option::Option<::std::string::String> {
        &self.status
    }
    /// <p>The creation date and time (in Unix time) of the solution.</p>
    pub fn creation_date_time(mut self, input: ::aws_smithy_types::DateTime) -> Self {
        self.creation_date_time = ::std::option::Option::Some(input);
        self
    }
    /// <p>The creation date and time (in Unix time) of the solution.</p>
    pub fn set_creation_date_time(mut self, input: ::std::option::Option<::aws_smithy_types::DateTime>) -> Self {
        self.creation_date_time = input;
        self
    }
    /// <p>The creation date and time (in Unix time) of the solution.</p>
    pub fn get_creation_date_time(&self) -> &::std::option::Option<::aws_smithy_types::DateTime> {
        &self.creation_date_time
    }
    /// <p>The date and time (in Unix time) that the solution was last updated.</p>
    pub fn last_updated_date_time(mut self, input: ::aws_smithy_types::DateTime) -> Self {
        self.last_updated_date_time = ::std::option::Option::Some(input);
        self
    }
    /// <p>The date and time (in Unix time) that the solution was last updated.</p>
    pub fn set_last_updated_date_time(mut self, input: ::std::option::Option<::aws_smithy_types::DateTime>) -> Self {
        self.last_updated_date_time = input;
        self
    }
    /// <p>The date and time (in Unix time) that the solution was last updated.</p>
    pub fn get_last_updated_date_time(&self) -> &::std::option::Option<::aws_smithy_types::DateTime> {
        &self.last_updated_date_time
    }
    /// <p>Describes the latest version of the solution, including the status and the ARN.</p>
    pub fn latest_solution_version(mut self, input: crate::types::SolutionVersionSummary) -> Self {
        self.latest_solution_version = ::std::option::Option::Some(input);
        self
    }
    /// <p>Describes the latest version of the solution, including the status and the ARN.</p>
    pub fn set_latest_solution_version(mut self, input: ::std::option::Option<crate::types::SolutionVersionSummary>) -> Self {
        self.latest_solution_version = input;
        self
    }
    /// <p>Describes the latest version of the solution, including the status and the ARN.</p>
    pub fn get_latest_solution_version(&self) -> &::std::option::Option<crate::types::SolutionVersionSummary> {
        &self.latest_solution_version
    }
    /// <p>Provides a summary of the latest updates to the solution.</p>
    pub fn latest_solution_update(mut self, input: crate::types::SolutionUpdateSummary) -> Self {
        self.latest_solution_update = ::std::option::Option::Some(input);
        self
    }
    /// <p>Provides a summary of the latest updates to the solution.</p>
    pub fn set_latest_solution_update(mut self, input: ::std::option::Option<crate::types::SolutionUpdateSummary>) -> Self {
        self.latest_solution_update = input;
        self
    }
    /// <p>Provides a summary of the latest updates to the solution.</p>
    pub fn get_latest_solution_update(&self) -> &::std::option::Option<crate::types::SolutionUpdateSummary> {
        &self.latest_solution_update
    }
    /// Consumes the builder and constructs a [`Solution`](crate::types::Solution).
    pub fn build(self) -> crate::types::Solution {
        crate::types::Solution {
            name: self.name,
            solution_arn: self.solution_arn,
            perform_hpo: self.perform_hpo.unwrap_or_default(),
            perform_auto_ml: self.perform_auto_ml.unwrap_or_default(),
            perform_auto_training: self.perform_auto_training,
            perform_incremental_update: self.perform_incremental_update,
            recipe_arn: self.recipe_arn,
            dataset_group_arn: self.dataset_group_arn,
            event_type: self.event_type,
            solution_config: self.solution_config,
            auto_ml_result: self.auto_ml_result,
            status: self.status,
            creation_date_time: self.creation_date_time,
            last_updated_date_time: self.last_updated_date_time,
            latest_solution_version: self.latest_solution_version,
            latest_solution_update: self.latest_solution_update,
        }
    }
}