aws_sdk_machinelearning::types::builders

Struct MlModelBuilder

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#[non_exhaustive]
pub struct MlModelBuilder { /* private fields */ }
Expand description

A builder for MlModel.

Implementations§

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impl MlModelBuilder

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

The ID assigned to the MLModel at creation.

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

The ID assigned to the MLModel at creation.

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

The ID assigned to the MLModel at creation.

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

The ID of the training DataSource. The CreateMLModel operation uses the TrainingDataSourceId.

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

The ID of the training DataSource. The CreateMLModel operation uses the TrainingDataSourceId.

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

The ID of the training DataSource. The CreateMLModel operation uses the TrainingDataSourceId.

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

The AWS user account from which the MLModel was created. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account.

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

The AWS user account from which the MLModel was created. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account.

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

The AWS user account from which the MLModel was created. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account.

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pub fn created_at(self, input: DateTime) -> Self

The time that the MLModel was created. The time is expressed in epoch time.

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pub fn set_created_at(self, input: Option<DateTime>) -> Self

The time that the MLModel was created. The time is expressed in epoch time.

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pub fn get_created_at(&self) -> &Option<DateTime>

The time that the MLModel was created. The time is expressed in epoch time.

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pub fn last_updated_at(self, input: DateTime) -> Self

The time of the most recent edit to the MLModel. The time is expressed in epoch time.

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pub fn set_last_updated_at(self, input: Option<DateTime>) -> Self

The time of the most recent edit to the MLModel. The time is expressed in epoch time.

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pub fn get_last_updated_at(&self) -> &Option<DateTime>

The time of the most recent edit to the MLModel. The time is expressed in epoch time.

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

A user-supplied name or description of the MLModel.

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

A user-supplied name or description of the MLModel.

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

A user-supplied name or description of the MLModel.

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pub fn status(self, input: EntityStatus) -> Self

The current status of an MLModel. This element can have one of the following values:

  • PENDING - Amazon Machine Learning (Amazon ML) submitted a request to create an MLModel.

  • INPROGRESS - The creation process is underway.

  • FAILED - The request to create an MLModel didn't run to completion. The model isn't usable.

  • COMPLETED - The creation process completed successfully.

  • DELETED - The MLModel is marked as deleted. It isn't usable.

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pub fn set_status(self, input: Option<EntityStatus>) -> Self

The current status of an MLModel. This element can have one of the following values:

  • PENDING - Amazon Machine Learning (Amazon ML) submitted a request to create an MLModel.

  • INPROGRESS - The creation process is underway.

  • FAILED - The request to create an MLModel didn't run to completion. The model isn't usable.

  • COMPLETED - The creation process completed successfully.

  • DELETED - The MLModel is marked as deleted. It isn't usable.

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pub fn get_status(&self) -> &Option<EntityStatus>

The current status of an MLModel. This element can have one of the following values:

  • PENDING - Amazon Machine Learning (Amazon ML) submitted a request to create an MLModel.

  • INPROGRESS - The creation process is underway.

  • FAILED - The request to create an MLModel didn't run to completion. The model isn't usable.

  • COMPLETED - The creation process completed successfully.

  • DELETED - The MLModel is marked as deleted. It isn't usable.

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pub fn size_in_bytes(self, input: i64) -> Self

Long integer type that is a 64-bit signed number.

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pub fn set_size_in_bytes(self, input: Option<i64>) -> Self

Long integer type that is a 64-bit signed number.

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pub fn get_size_in_bytes(&self) -> &Option<i64>

Long integer type that is a 64-bit signed number.

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pub fn endpoint_info(self, input: RealtimeEndpointInfo) -> Self

The current endpoint of the MLModel.

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pub fn set_endpoint_info(self, input: Option<RealtimeEndpointInfo>) -> Self

The current endpoint of the MLModel.

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pub fn get_endpoint_info(&self) -> &Option<RealtimeEndpointInfo>

The current endpoint of the MLModel.

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pub fn training_parameters( self, k: impl Into<String>, v: impl Into<String>, ) -> Self

Adds a key-value pair to training_parameters.

To override the contents of this collection use set_training_parameters.

A list of the training parameters in the MLModel. The list is implemented as a map of key-value pairs.

The following is the current set of training parameters:

  • sgd.maxMLModelSizeInBytes - The maximum allowed size of the model. Depending on the input data, the size of the model might affect its performance.

    The value is an integer that ranges from 100000 to 2147483648. The default value is 33554432.

  • sgd.maxPasses - The number of times that the training process traverses the observations to build the MLModel. The value is an integer that ranges from 1 to 10000. The default value is 10.

  • sgd.shuffleType - 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 auto and none. The default value is none.

  • sgd.l1RegularizationAmount - The coefficient regularization L1 norm, which controls overfitting the data by penalizing large coefficients. This parameter tends to drive coefficients to zero, resulting in sparse feature set. If you use this parameter, start by specifying a small value, such as 1.0E-08.

    The value is a double that ranges from 0 to MAX_DOUBLE. The default is to not use L1 normalization. This parameter can't be used when L2 is specified. Use this parameter sparingly.

  • sgd.l2RegularizationAmount - The coefficient regularization L2 norm, which 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 1.0E-08.

    The value is a double that ranges from 0 to MAX_DOUBLE. The default is to not use L2 normalization. This parameter can't be used when L1 is specified. Use this parameter sparingly.

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

A list of the training parameters in the MLModel. The list is implemented as a map of key-value pairs.

The following is the current set of training parameters:

  • sgd.maxMLModelSizeInBytes - The maximum allowed size of the model. Depending on the input data, the size of the model might affect its performance.

    The value is an integer that ranges from 100000 to 2147483648. The default value is 33554432.

  • sgd.maxPasses - The number of times that the training process traverses the observations to build the MLModel. The value is an integer that ranges from 1 to 10000. The default value is 10.

  • sgd.shuffleType - 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 auto and none. The default value is none.

  • sgd.l1RegularizationAmount - The coefficient regularization L1 norm, which controls overfitting the data by penalizing large coefficients. This parameter tends to drive coefficients to zero, resulting in sparse feature set. If you use this parameter, start by specifying a small value, such as 1.0E-08.

    The value is a double that ranges from 0 to MAX_DOUBLE. The default is to not use L1 normalization. This parameter can't be used when L2 is specified. Use this parameter sparingly.

  • sgd.l2RegularizationAmount - The coefficient regularization L2 norm, which 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 1.0E-08.

    The value is a double that ranges from 0 to MAX_DOUBLE. The default is to not use L2 normalization. This parameter can't be used when L1 is specified. Use this parameter sparingly.

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pub fn get_training_parameters(&self) -> &Option<HashMap<String, String>>

A list of the training parameters in the MLModel. The list is implemented as a map of key-value pairs.

The following is the current set of training parameters:

  • sgd.maxMLModelSizeInBytes - The maximum allowed size of the model. Depending on the input data, the size of the model might affect its performance.

    The value is an integer that ranges from 100000 to 2147483648. The default value is 33554432.

  • sgd.maxPasses - The number of times that the training process traverses the observations to build the MLModel. The value is an integer that ranges from 1 to 10000. The default value is 10.

  • sgd.shuffleType - 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 auto and none. The default value is none.

  • sgd.l1RegularizationAmount - The coefficient regularization L1 norm, which controls overfitting the data by penalizing large coefficients. This parameter tends to drive coefficients to zero, resulting in sparse feature set. If you use this parameter, start by specifying a small value, such as 1.0E-08.

    The value is a double that ranges from 0 to MAX_DOUBLE. The default is to not use L1 normalization. This parameter can't be used when L2 is specified. Use this parameter sparingly.

  • sgd.l2RegularizationAmount - The coefficient regularization L2 norm, which 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 1.0E-08.

    The value is a double that ranges from 0 to MAX_DOUBLE. The default is to not use L2 normalization. This parameter can't be used when L1 is specified. Use this parameter sparingly.

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pub fn input_data_location_s3(self, input: impl Into<String>) -> Self

The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).

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pub fn set_input_data_location_s3(self, input: Option<String>) -> Self

The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).

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pub fn get_input_data_location_s3(&self) -> &Option<String>

The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).

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pub fn algorithm(self, input: Algorithm) -> Self

The algorithm used to train the MLModel. The following algorithm is supported:

  • SGD -- Stochastic gradient descent. The goal of SGD is to minimize the gradient of the loss function.

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pub fn set_algorithm(self, input: Option<Algorithm>) -> Self

The algorithm used to train the MLModel. The following algorithm is supported:

  • SGD -- Stochastic gradient descent. The goal of SGD is to minimize the gradient of the loss function.

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pub fn get_algorithm(&self) -> &Option<Algorithm>

The algorithm used to train the MLModel. The following algorithm is supported:

  • SGD -- Stochastic gradient descent. The goal of SGD is to minimize the gradient of the loss function.

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pub fn ml_model_type(self, input: MlModelType) -> Self

Identifies the MLModel category. The following are the available types:

  • REGRESSION - Produces a numeric result. For example, "What price should a house be listed at?"

  • BINARY - Produces one of two possible results. For example, "Is this a child-friendly web site?".

  • MULTICLASS - Produces one of several possible results. For example, "Is this a HIGH-, LOW-, or MEDIUM-risk trade?".

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pub fn set_ml_model_type(self, input: Option<MlModelType>) -> Self

Identifies the MLModel category. The following are the available types:

  • REGRESSION - Produces a numeric result. For example, "What price should a house be listed at?"

  • BINARY - Produces one of two possible results. For example, "Is this a child-friendly web site?".

  • MULTICLASS - Produces one of several possible results. For example, "Is this a HIGH-, LOW-, or MEDIUM-risk trade?".

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pub fn get_ml_model_type(&self) -> &Option<MlModelType>

Identifies the MLModel category. The following are the available types:

  • REGRESSION - Produces a numeric result. For example, "What price should a house be listed at?"

  • BINARY - Produces one of two possible results. For example, "Is this a child-friendly web site?".

  • MULTICLASS - Produces one of several possible results. For example, "Is this a HIGH-, LOW-, or MEDIUM-risk trade?".

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pub fn score_threshold(self, input: f32) -> Self

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pub fn set_score_threshold(self, input: Option<f32>) -> Self

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pub fn get_score_threshold(&self) -> &Option<f32>

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pub fn score_threshold_last_updated_at(self, input: DateTime) -> Self

The time of the most recent edit to the ScoreThreshold. The time is expressed in epoch time.

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pub fn set_score_threshold_last_updated_at( self, input: Option<DateTime>, ) -> Self

The time of the most recent edit to the ScoreThreshold. The time is expressed in epoch time.

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pub fn get_score_threshold_last_updated_at(&self) -> &Option<DateTime>

The time of the most recent edit to the ScoreThreshold. The time is expressed in epoch time.

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

A description of the most recent details about accessing the MLModel.

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

A description of the most recent details about accessing the MLModel.

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

A description of the most recent details about accessing the MLModel.

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pub fn compute_time(self, input: i64) -> Self

Long integer type that is a 64-bit signed number.

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pub fn set_compute_time(self, input: Option<i64>) -> Self

Long integer type that is a 64-bit signed number.

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pub fn get_compute_time(&self) -> &Option<i64>

Long integer type that is a 64-bit signed number.

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pub fn finished_at(self, input: DateTime) -> Self

A timestamp represented in epoch time.

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pub fn set_finished_at(self, input: Option<DateTime>) -> Self

A timestamp represented in epoch time.

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pub fn get_finished_at(&self) -> &Option<DateTime>

A timestamp represented in epoch time.

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pub fn started_at(self, input: DateTime) -> Self

A timestamp represented in epoch time.

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pub fn set_started_at(self, input: Option<DateTime>) -> Self

A timestamp represented in epoch time.

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pub fn get_started_at(&self) -> &Option<DateTime>

A timestamp represented in epoch time.

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pub fn build(self) -> MlModel

Consumes the builder and constructs a MlModel.

Trait Implementations§

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impl Clone for MlModelBuilder

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fn clone(&self) -> MlModelBuilder

Returns a duplicate of the value. Read more
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fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source. Read more
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impl Debug for MlModelBuilder

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

Formats the value using the given formatter. Read more
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impl Default for MlModelBuilder

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fn default() -> MlModelBuilder

Returns the “default value” for a type. Read more
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impl PartialEq for MlModelBuilder

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

Tests for self and other values to be equal, and is used by ==.
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fn ne(&self, other: &Rhs) -> bool

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

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👎Deprecated since 1.0.1: renamed to resetting() due to conflicts with Vec::clear(). The clear() method will be removed in a future release.

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