Struct aws_sdk_machinelearning::types::MlModel

#[non_exhaustive]
pub struct MlModel {

    pub ml_model_id: Option<String>,
    pub training_data_source_id: Option<String>,
    pub created_by_iam_user: Option<String>,
    pub created_at: Option<DateTime>,
    pub last_updated_at: Option<DateTime>,
    pub name: Option<String>,
    pub status: Option<EntityStatus>,
    pub size_in_bytes: Option<i64>,
    pub endpoint_info: Option<RealtimeEndpointInfo>,
    pub training_parameters: Option<HashMap<String, String>>,
    pub input_data_location_s3: Option<String>,
    pub algorithm: Option<Algorithm>,
    pub ml_model_type: Option<MlModelType>,
    pub score_threshold: Option<f32>,
    pub score_threshold_last_updated_at: Option<DateTime>,
    pub message: Option<String>,
    pub compute_time: Option<i64>,
    pub finished_at: Option<DateTime>,
    pub started_at: Option<DateTime>,
}

Represents the output of a GetMLModel operation.

The content consists of the detailed metadata and the current status of the MLModel.

Fields (Non-exhaustive)

Non-exhaustive structs could have additional fields added in future. Therefore, non-exhaustive structs cannot be constructed in external crates using the traditional Struct { .. } syntax; cannot be matched against without a wildcard ..; and struct update syntax will not work.

ml_model_id: Option<String>

The ID assigned to the MLModel at creation.

training_data_source_id: Option<String>

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

created_by_iam_user: 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.

created_at: Option<DateTime>

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

last_updated_at: Option<DateTime>

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

name: Option<String>

A user-supplied name or description of the MLModel.

status: 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.

size_in_bytes: Option<i64>

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

endpoint_info: Option<RealtimeEndpointInfo>

The current endpoint of the MLModel.

training_parameters: 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.

input_data_location_s3: Option<String>

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

algorithm: 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.

ml_model_type: 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?".

score_threshold: Option<f32>

score_threshold_last_updated_at: Option<DateTime>

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

message: Option<String>

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

compute_time: Option<i64>

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

finished_at: Option<DateTime>

A timestamp represented in epoch time.

started_at: Option<DateTime>

A timestamp represented in epoch time.

Implementations

impl MlModel

pub fn ml_model_id(&self) -> Option<&str>

The ID assigned to the MLModel at creation.

pub fn training_data_source_id(&self) -> Option<&str>

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

pub fn created_by_iam_user(&self) -> Option<&str>

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.

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

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

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

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

pub fn name(&self) -> Option<&str>

A user-supplied name or description of the MLModel.

pub fn 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.

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

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

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

The current endpoint of the MLModel.

pub fn 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.

pub fn input_data_location_s3(&self) -> Option<&str>

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

pub fn 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.

pub fn 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?".

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

pub fn 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.

pub fn message(&self) -> Option<&str>

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

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

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

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

A timestamp represented in epoch time.

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

A timestamp represented in epoch time.

impl MlModel

pub fn builder() -> MlModelBuilder

Creates a new builder-style object to manufacture MlModel.

Trait Implementations

impl Clone for MlModel

fn clone(&self) -> MlModel

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for MlModel

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

Formats the value using the given formatter.

impl PartialEq for MlModel

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

This method tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

This method tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl StructuralPartialEq for MlModel