Struct aws_sdk_machinelearning::output::GetMlModelOutput
source · [−]#[non_exhaustive]pub struct GetMlModelOutput {Show 21 fields
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 ml_model_type: Option<MlModelType>,
pub score_threshold: Option<f32>,
pub score_threshold_last_updated_at: Option<DateTime>,
pub log_uri: Option<String>,
pub message: Option<String>,
pub compute_time: Option<i64>,
pub finished_at: Option<DateTime>,
pub started_at: Option<DateTime>,
pub recipe: Option<String>,
pub schema: Option<String>,
}
Expand description
Represents the output of a GetMLModel
operation, and provides detailed information about a MLModel
.
Fields (Non-exhaustive)
This struct is marked as non-exhaustive
Struct { .. }
syntax; cannot be matched against without a wildcard ..
; and struct update syntax will not work.ml_model_id: Option<String>
The MLModel ID, which is same as the MLModelId
in the request.
training_data_source_id: Option<String>
The ID of the training DataSource
.
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 the MLModel
. This element can have one of the following values:
-
PENDING
- Amazon Machine Learning (Amazon ML) submitted a request to describe aMLModel
. -
INPROGRESS
- The request is processing. -
FAILED
- The request did not run to completion. The ML model isn't usable. -
COMPLETED
- The request completed successfully. -
DELETED
- TheMLModel
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
to2147483648
. The default value is33554432
. -
sgd.maxPasses
- The number of times that the training process traverses the observations to build theMLModel
. The value is an integer that ranges from1
to10000
. The default value is10
. -
sgd.shuffleType
- Whether Amazon ML shuffles the training data. Shuffling data improves a model's ability to find the optimal solution for a variety of data types. The valid values areauto
andnone
. The default value isnone
. We strongly recommend that you shuffle your data. -
sgd.l1RegularizationAmount
- 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 as1.0E-08
.The value is a double that ranges from
0
toMAX_DOUBLE
. The default is to not use L1 normalization. This parameter can't be used whenL2
is specified. Use this parameter sparingly. -
sgd.l2RegularizationAmount
- 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 as1.0E-08
.The value is a double that ranges from
0
toMAX_DOUBLE
. The default is to not use L2 normalization. This parameter can't be used whenL1
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).
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 an e-commerce website?"
-
MULTICLASS -- Produces one of several possible results. For example, "Is this a HIGH, LOW or MEDIUM risk trade?"
score_threshold: Option<f32>
The scoring threshold is used in binary classification MLModel
models. It marks the boundary between a positive prediction and a negative prediction.
Output values greater than or equal to the threshold receive a positive result from the MLModel, such as true
. Output values less than the threshold receive a negative response from the MLModel, such as false
.
score_threshold_last_updated_at: Option<DateTime>
The time of the most recent edit to the ScoreThreshold
. The time is expressed in epoch time.
log_uri: Option<String>
A link to the file that contains logs of the CreateMLModel
operation.
message: Option<String>
A description of the most recent details about accessing the MLModel
.
compute_time: Option<i64>
The approximate CPU time in milliseconds that Amazon Machine Learning spent processing the MLModel
, normalized and scaled on computation resources. ComputeTime
is only available if the MLModel
is in the COMPLETED
state.
finished_at: Option<DateTime>
The epoch time when Amazon Machine Learning marked the MLModel
as COMPLETED
or FAILED
. FinishedAt
is only available when the MLModel
is in the COMPLETED
or FAILED
state.
started_at: Option<DateTime>
The epoch time when Amazon Machine Learning marked the MLModel
as INPROGRESS
. StartedAt
isn't available if the MLModel
is in the PENDING
state.
recipe: Option<String>
The recipe to use when training the MLModel
. The Recipe
provides detailed information about the observation data to use during training, and manipulations to perform on the observation data during training.
Note: This parameter is provided as part of the verbose format.
schema: Option<String>
The schema used by all of the data files referenced by the DataSource
.
Note: This parameter is provided as part of the verbose format.
Implementations
The MLModel ID, which is same as the MLModelId
in the request.
The ID of the training DataSource
.
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.
The time that the MLModel
was created. The time is expressed in epoch time.
The time of the most recent edit to the MLModel
. The time is expressed in epoch time.
The current status of the MLModel
. This element can have one of the following values:
-
PENDING
- Amazon Machine Learning (Amazon ML) submitted a request to describe aMLModel
. -
INPROGRESS
- The request is processing. -
FAILED
- The request did not run to completion. The ML model isn't usable. -
COMPLETED
- The request completed successfully. -
DELETED
- TheMLModel
is marked as deleted. It isn't usable.
Long integer type that is a 64-bit signed number.
The current endpoint of the MLModel
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
to2147483648
. The default value is33554432
. -
sgd.maxPasses
- The number of times that the training process traverses the observations to build theMLModel
. The value is an integer that ranges from1
to10000
. The default value is10
. -
sgd.shuffleType
- Whether Amazon ML shuffles the training data. Shuffling data improves a model's ability to find the optimal solution for a variety of data types. The valid values areauto
andnone
. The default value isnone
. We strongly recommend that you shuffle your data. -
sgd.l1RegularizationAmount
- 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 as1.0E-08
.The value is a double that ranges from
0
toMAX_DOUBLE
. The default is to not use L1 normalization. This parameter can't be used whenL2
is specified. Use this parameter sparingly. -
sgd.l2RegularizationAmount
- 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 as1.0E-08
.The value is a double that ranges from
0
toMAX_DOUBLE
. The default is to not use L2 normalization. This parameter can't be used whenL1
is specified. Use this parameter sparingly.
The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).
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 an e-commerce website?"
-
MULTICLASS -- Produces one of several possible results. For example, "Is this a HIGH, LOW or MEDIUM risk trade?"
The scoring threshold is used in binary classification MLModel
models. It marks the boundary between a positive prediction and a negative prediction.
Output values greater than or equal to the threshold receive a positive result from the MLModel, such as true
. Output values less than the threshold receive a negative response from the MLModel, such as false
.
The time of the most recent edit to the ScoreThreshold
. The time is expressed in epoch time.
A link to the file that contains logs of the CreateMLModel
operation.
A description of the most recent details about accessing the MLModel
.
The approximate CPU time in milliseconds that Amazon Machine Learning spent processing the MLModel
, normalized and scaled on computation resources. ComputeTime
is only available if the MLModel
is in the COMPLETED
state.
The epoch time when Amazon Machine Learning marked the MLModel
as COMPLETED
or FAILED
. FinishedAt
is only available when the MLModel
is in the COMPLETED
or FAILED
state.
The epoch time when Amazon Machine Learning marked the MLModel
as INPROGRESS
. StartedAt
isn't available if the MLModel
is in the PENDING
state.
The recipe to use when training the MLModel
. The Recipe
provides detailed information about the observation data to use during training, and manipulations to perform on the observation data during training.
Note: This parameter is provided as part of the verbose format.
Creates a new builder-style object to manufacture GetMlModelOutput
Trait Implementations
This method tests for self
and other
values to be equal, and is used
by ==
. Read more
This method tests for !=
.
Auto Trait Implementations
impl RefUnwindSafe for GetMlModelOutput
impl Send for GetMlModelOutput
impl Sync for GetMlModelOutput
impl Unpin for GetMlModelOutput
impl UnwindSafe for GetMlModelOutput
Blanket Implementations
Mutably borrows from an owned value. Read more
Attaches the provided Subscriber
to this type, returning a
WithDispatch
wrapper. Read more
Attaches the current default Subscriber
to this type, returning a
WithDispatch
wrapper. Read more