// Code generated by software.amazon.smithy.rust.codegen.smithy-rs. DO NOT EDIT.
pub use crate::operation::create_ml_model::_create_ml_model_output::CreateMlModelOutputBuilder;

pub use crate::operation::create_ml_model::_create_ml_model_input::CreateMlModelInputBuilder;

impl CreateMlModelInputBuilder {
    /// Sends a request with this input using the given client.
    pub async fn send_with(
        self,
        client: &crate::Client,
    ) -> ::std::result::Result<
        crate::operation::create_ml_model::CreateMlModelOutput,
        ::aws_smithy_runtime_api::client::result::SdkError<
            crate::operation::create_ml_model::CreateMLModelError,
            ::aws_smithy_runtime_api::client::orchestrator::HttpResponse,
        >,
    > {
        let mut fluent_builder = client.create_ml_model();
        fluent_builder.inner = self;
        fluent_builder.send().await
    }
}
/// Fluent builder constructing a request to `CreateMLModel`.
///
/// <p>Creates a new <code>MLModel</code> using the <code>DataSource</code> and the recipe as information sources. </p>
/// <p>An <code>MLModel</code> is nearly immutable. Users can update only the <code>MLModelName</code> and the <code>ScoreThreshold</code> in an <code>MLModel</code> without creating a new <code>MLModel</code>. </p>
/// <p> <code>CreateMLModel</code> is an asynchronous operation. In response to <code>CreateMLModel</code>, Amazon Machine Learning (Amazon ML) immediately returns and sets the <code>MLModel</code> status to <code>PENDING</code>. After the <code>MLModel</code> has been created and ready is for use, Amazon ML sets the status to <code>COMPLETED</code>. </p>
/// <p>You can use the <code>GetMLModel</code> operation to check the progress of the <code>MLModel</code> during the creation operation.</p>
/// <p> <code>CreateMLModel</code> requires a <code>DataSource</code> with computed statistics, which can be created by setting <code>ComputeStatistics</code> to <code>true</code> in <code>CreateDataSourceFromRDS</code>, <code>CreateDataSourceFromS3</code>, or <code>CreateDataSourceFromRedshift</code> operations. </p>
#[derive(::std::clone::Clone, ::std::fmt::Debug)]
pub struct CreateMLModelFluentBuilder {
    handle: ::std::sync::Arc<crate::client::Handle>,
    inner: crate::operation::create_ml_model::builders::CreateMlModelInputBuilder,
    config_override: ::std::option::Option<crate::config::Builder>,
}
impl
    crate::client::customize::internal::CustomizableSend<
        crate::operation::create_ml_model::CreateMlModelOutput,
        crate::operation::create_ml_model::CreateMLModelError,
    > for CreateMLModelFluentBuilder
{
    fn send(
        self,
        config_override: crate::config::Builder,
    ) -> crate::client::customize::internal::BoxFuture<
        crate::client::customize::internal::SendResult<
            crate::operation::create_ml_model::CreateMlModelOutput,
            crate::operation::create_ml_model::CreateMLModelError,
        >,
    > {
        ::std::boxed::Box::pin(async move { self.config_override(config_override).send().await })
    }
}
impl CreateMLModelFluentBuilder {
    /// Creates a new `CreateMLModel`.
    pub(crate) fn new(handle: ::std::sync::Arc<crate::client::Handle>) -> Self {
        Self {
            handle,
            inner: ::std::default::Default::default(),
            config_override: ::std::option::Option::None,
        }
    }
    /// Access the CreateMLModel as a reference.
    pub fn as_input(&self) -> &crate::operation::create_ml_model::builders::CreateMlModelInputBuilder {
        &self.inner
    }
    /// 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](aws_smithy_types::retry::RetryConfig), which can be
    /// set when configuring the client.
    pub async fn send(
        self,
    ) -> ::std::result::Result<
        crate::operation::create_ml_model::CreateMlModelOutput,
        ::aws_smithy_runtime_api::client::result::SdkError<
            crate::operation::create_ml_model::CreateMLModelError,
            ::aws_smithy_runtime_api::client::orchestrator::HttpResponse,
        >,
    > {
        let input = self
            .inner
            .build()
            .map_err(::aws_smithy_runtime_api::client::result::SdkError::construction_failure)?;
        let runtime_plugins = crate::operation::create_ml_model::CreateMLModel::operation_runtime_plugins(
            self.handle.runtime_plugins.clone(),
            &self.handle.conf,
            self.config_override,
        );
        crate::operation::create_ml_model::CreateMLModel::orchestrate(&runtime_plugins, input).await
    }

    /// Consumes this builder, creating a customizable operation that can be modified before being sent.
    pub fn customize(
        self,
    ) -> crate::client::customize::CustomizableOperation<
        crate::operation::create_ml_model::CreateMlModelOutput,
        crate::operation::create_ml_model::CreateMLModelError,
        Self,
    > {
        crate::client::customize::CustomizableOperation::new(self)
    }
    pub(crate) fn config_override(mut self, config_override: impl Into<crate::config::Builder>) -> Self {
        self.set_config_override(Some(config_override.into()));
        self
    }

    pub(crate) fn set_config_override(&mut self, config_override: Option<crate::config::Builder>) -> &mut Self {
        self.config_override = config_override;
        self
    }
    /// <p>A user-supplied ID that uniquely identifies the <code>MLModel</code>.</p>
    pub fn ml_model_id(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {
        self.inner = self.inner.ml_model_id(input.into());
        self
    }
    /// <p>A user-supplied ID that uniquely identifies the <code>MLModel</code>.</p>
    pub fn set_ml_model_id(mut self, input: ::std::option::Option<::std::string::String>) -> Self {
        self.inner = self.inner.set_ml_model_id(input);
        self
    }
    /// <p>A user-supplied ID that uniquely identifies the <code>MLModel</code>.</p>
    pub fn get_ml_model_id(&self) -> &::std::option::Option<::std::string::String> {
        self.inner.get_ml_model_id()
    }
    /// <p>A user-supplied name or description of the <code>MLModel</code>.</p>
    pub fn ml_model_name(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {
        self.inner = self.inner.ml_model_name(input.into());
        self
    }
    /// <p>A user-supplied name or description of the <code>MLModel</code>.</p>
    pub fn set_ml_model_name(mut self, input: ::std::option::Option<::std::string::String>) -> Self {
        self.inner = self.inner.set_ml_model_name(input);
        self
    }
    /// <p>A user-supplied name or description of the <code>MLModel</code>.</p>
    pub fn get_ml_model_name(&self) -> &::std::option::Option<::std::string::String> {
        self.inner.get_ml_model_name()
    }
    /// <p>The category of supervised learning that this <code>MLModel</code> will address. Choose from the following types:</p>
    /// <ul>
    /// <li> <p>Choose <code>REGRESSION</code> if the <code>MLModel</code> will be used to predict a numeric value.</p> </li>
    /// <li> <p>Choose <code>BINARY</code> if the <code>MLModel</code> result has two possible values.</p> </li>
    /// <li> <p>Choose <code>MULTICLASS</code> if the <code>MLModel</code> result has a limited number of values.</p> </li>
    /// </ul>
    /// <p> For more information, see the <a href="https://docs.aws.amazon.com/machine-learning/latest/dg">Amazon Machine Learning Developer Guide</a>.</p>
    pub fn ml_model_type(mut self, input: crate::types::MlModelType) -> Self {
        self.inner = self.inner.ml_model_type(input);
        self
    }
    /// <p>The category of supervised learning that this <code>MLModel</code> will address. Choose from the following types:</p>
    /// <ul>
    /// <li> <p>Choose <code>REGRESSION</code> if the <code>MLModel</code> will be used to predict a numeric value.</p> </li>
    /// <li> <p>Choose <code>BINARY</code> if the <code>MLModel</code> result has two possible values.</p> </li>
    /// <li> <p>Choose <code>MULTICLASS</code> if the <code>MLModel</code> result has a limited number of values.</p> </li>
    /// </ul>
    /// <p> For more information, see the <a href="https://docs.aws.amazon.com/machine-learning/latest/dg">Amazon Machine Learning Developer Guide</a>.</p>
    pub fn set_ml_model_type(mut self, input: ::std::option::Option<crate::types::MlModelType>) -> Self {
        self.inner = self.inner.set_ml_model_type(input);
        self
    }
    /// <p>The category of supervised learning that this <code>MLModel</code> will address. Choose from the following types:</p>
    /// <ul>
    /// <li> <p>Choose <code>REGRESSION</code> if the <code>MLModel</code> will be used to predict a numeric value.</p> </li>
    /// <li> <p>Choose <code>BINARY</code> if the <code>MLModel</code> result has two possible values.</p> </li>
    /// <li> <p>Choose <code>MULTICLASS</code> if the <code>MLModel</code> result has a limited number of values.</p> </li>
    /// </ul>
    /// <p> For more information, see the <a href="https://docs.aws.amazon.com/machine-learning/latest/dg">Amazon Machine Learning Developer Guide</a>.</p>
    pub fn get_ml_model_type(&self) -> &::std::option::Option<crate::types::MlModelType> {
        self.inner.get_ml_model_type()
    }
    /// Adds a key-value pair to `Parameters`.
    ///
    /// To override the contents of this collection use [`set_parameters`](Self::set_parameters).
    ///
    /// <p>A list of the training parameters in the <code>MLModel</code>. The list is implemented as a map of key-value pairs.</p>
    /// <p>The following is the current set of training parameters:</p>
    /// <ul>
    /// <li> <p> <code>sgd.maxMLModelSizeInBytes</code> - The maximum allowed size of the model. Depending on the input data, the size of the model might affect its performance.</p> <p> The value is an integer that ranges from <code>100000</code> to <code>2147483648</code>. The default value is <code>33554432</code>.</p> </li>
    /// <li> <p> <code>sgd.maxPasses</code> - The number of times that the training process traverses the observations to build the <code>MLModel</code>. The value is an integer that ranges from <code>1</code> to <code>10000</code>. The default value is <code>10</code>.</p> </li>
    /// <li> <p> <code>sgd.shuffleType</code> - Whether Amazon ML shuffles the training data. Shuffling the data improves a model's ability to find the optimal solution for a variety of data types. The valid values are <code>auto</code> and <code>none</code>. The default value is <code>none</code>. We strongly recommend that you shuffle your data.</p> </li>
    /// <li> <p> <code>sgd.l1RegularizationAmount</code> - The coefficient regularization L1 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to zero, resulting in a sparse feature set. If you use this parameter, start by specifying a small value, such as <code>1.0E-08</code>.</p> <p>The value is a double that ranges from <code>0</code> to <code>MAX_DOUBLE</code>. The default is to not use L1 normalization. This parameter can't be used when <code>L2</code> is specified. Use this parameter sparingly.</p> </li>
    /// <li> <p> <code>sgd.l2RegularizationAmount</code> - The coefficient regularization L2 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to small, nonzero values. If you use this parameter, start by specifying a small value, such as <code>1.0E-08</code>.</p> <p>The value is a double that ranges from <code>0</code> to <code>MAX_DOUBLE</code>. The default is to not use L2 normalization. This parameter can't be used when <code>L1</code> is specified. Use this parameter sparingly.</p> </li>
    /// </ul>
    pub fn parameters(mut self, k: impl ::std::convert::Into<::std::string::String>, v: impl ::std::convert::Into<::std::string::String>) -> Self {
        self.inner = self.inner.parameters(k.into(), v.into());
        self
    }
    /// <p>A list of the training parameters in the <code>MLModel</code>. The list is implemented as a map of key-value pairs.</p>
    /// <p>The following is the current set of training parameters:</p>
    /// <ul>
    /// <li> <p> <code>sgd.maxMLModelSizeInBytes</code> - The maximum allowed size of the model. Depending on the input data, the size of the model might affect its performance.</p> <p> The value is an integer that ranges from <code>100000</code> to <code>2147483648</code>. The default value is <code>33554432</code>.</p> </li>
    /// <li> <p> <code>sgd.maxPasses</code> - The number of times that the training process traverses the observations to build the <code>MLModel</code>. The value is an integer that ranges from <code>1</code> to <code>10000</code>. The default value is <code>10</code>.</p> </li>
    /// <li> <p> <code>sgd.shuffleType</code> - Whether Amazon ML shuffles the training data. Shuffling the data improves a model's ability to find the optimal solution for a variety of data types. The valid values are <code>auto</code> and <code>none</code>. The default value is <code>none</code>. We strongly recommend that you shuffle your data.</p> </li>
    /// <li> <p> <code>sgd.l1RegularizationAmount</code> - The coefficient regularization L1 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to zero, resulting in a sparse feature set. If you use this parameter, start by specifying a small value, such as <code>1.0E-08</code>.</p> <p>The value is a double that ranges from <code>0</code> to <code>MAX_DOUBLE</code>. The default is to not use L1 normalization. This parameter can't be used when <code>L2</code> is specified. Use this parameter sparingly.</p> </li>
    /// <li> <p> <code>sgd.l2RegularizationAmount</code> - The coefficient regularization L2 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to small, nonzero values. If you use this parameter, start by specifying a small value, such as <code>1.0E-08</code>.</p> <p>The value is a double that ranges from <code>0</code> to <code>MAX_DOUBLE</code>. The default is to not use L2 normalization. This parameter can't be used when <code>L1</code> is specified. Use this parameter sparingly.</p> </li>
    /// </ul>
    pub fn set_parameters(mut self, input: ::std::option::Option<::std::collections::HashMap<::std::string::String, ::std::string::String>>) -> Self {
        self.inner = self.inner.set_parameters(input);
        self
    }
    /// <p>A list of the training parameters in the <code>MLModel</code>. The list is implemented as a map of key-value pairs.</p>
    /// <p>The following is the current set of training parameters:</p>
    /// <ul>
    /// <li> <p> <code>sgd.maxMLModelSizeInBytes</code> - The maximum allowed size of the model. Depending on the input data, the size of the model might affect its performance.</p> <p> The value is an integer that ranges from <code>100000</code> to <code>2147483648</code>. The default value is <code>33554432</code>.</p> </li>
    /// <li> <p> <code>sgd.maxPasses</code> - The number of times that the training process traverses the observations to build the <code>MLModel</code>. The value is an integer that ranges from <code>1</code> to <code>10000</code>. The default value is <code>10</code>.</p> </li>
    /// <li> <p> <code>sgd.shuffleType</code> - Whether Amazon ML shuffles the training data. Shuffling the data improves a model's ability to find the optimal solution for a variety of data types. The valid values are <code>auto</code> and <code>none</code>. The default value is <code>none</code>. We strongly recommend that you shuffle your data.</p> </li>
    /// <li> <p> <code>sgd.l1RegularizationAmount</code> - The coefficient regularization L1 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to zero, resulting in a sparse feature set. If you use this parameter, start by specifying a small value, such as <code>1.0E-08</code>.</p> <p>The value is a double that ranges from <code>0</code> to <code>MAX_DOUBLE</code>. The default is to not use L1 normalization. This parameter can't be used when <code>L2</code> is specified. Use this parameter sparingly.</p> </li>
    /// <li> <p> <code>sgd.l2RegularizationAmount</code> - The coefficient regularization L2 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to small, nonzero values. If you use this parameter, start by specifying a small value, such as <code>1.0E-08</code>.</p> <p>The value is a double that ranges from <code>0</code> to <code>MAX_DOUBLE</code>. The default is to not use L2 normalization. This parameter can't be used when <code>L1</code> is specified. Use this parameter sparingly.</p> </li>
    /// </ul>
    pub fn get_parameters(&self) -> &::std::option::Option<::std::collections::HashMap<::std::string::String, ::std::string::String>> {
        self.inner.get_parameters()
    }
    /// <p>The <code>DataSource</code> that points to the training data.</p>
    pub fn training_data_source_id(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {
        self.inner = self.inner.training_data_source_id(input.into());
        self
    }
    /// <p>The <code>DataSource</code> that points to the training data.</p>
    pub fn set_training_data_source_id(mut self, input: ::std::option::Option<::std::string::String>) -> Self {
        self.inner = self.inner.set_training_data_source_id(input);
        self
    }
    /// <p>The <code>DataSource</code> that points to the training data.</p>
    pub fn get_training_data_source_id(&self) -> &::std::option::Option<::std::string::String> {
        self.inner.get_training_data_source_id()
    }
    /// <p>The data recipe for creating the <code>MLModel</code>. You must specify either the recipe or its URI. If you don't specify a recipe or its URI, Amazon ML creates a default.</p>
    pub fn recipe(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {
        self.inner = self.inner.recipe(input.into());
        self
    }
    /// <p>The data recipe for creating the <code>MLModel</code>. You must specify either the recipe or its URI. If you don't specify a recipe or its URI, Amazon ML creates a default.</p>
    pub fn set_recipe(mut self, input: ::std::option::Option<::std::string::String>) -> Self {
        self.inner = self.inner.set_recipe(input);
        self
    }
    /// <p>The data recipe for creating the <code>MLModel</code>. You must specify either the recipe or its URI. If you don't specify a recipe or its URI, Amazon ML creates a default.</p>
    pub fn get_recipe(&self) -> &::std::option::Option<::std::string::String> {
        self.inner.get_recipe()
    }
    /// <p>The Amazon Simple Storage Service (Amazon S3) location and file name that contains the <code>MLModel</code> recipe. You must specify either the recipe or its URI. If you don't specify a recipe or its URI, Amazon ML creates a default.</p>
    pub fn recipe_uri(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {
        self.inner = self.inner.recipe_uri(input.into());
        self
    }
    /// <p>The Amazon Simple Storage Service (Amazon S3) location and file name that contains the <code>MLModel</code> recipe. You must specify either the recipe or its URI. If you don't specify a recipe or its URI, Amazon ML creates a default.</p>
    pub fn set_recipe_uri(mut self, input: ::std::option::Option<::std::string::String>) -> Self {
        self.inner = self.inner.set_recipe_uri(input);
        self
    }
    /// <p>The Amazon Simple Storage Service (Amazon S3) location and file name that contains the <code>MLModel</code> recipe. You must specify either the recipe or its URI. If you don't specify a recipe or its URI, Amazon ML creates a default.</p>
    pub fn get_recipe_uri(&self) -> &::std::option::Option<::std::string::String> {
        self.inner.get_recipe_uri()
    }
}