MachineLearning / Client / create_ml_model
create_ml_model#
- MachineLearning.Client.create_ml_model(**kwargs)#
Creates a new
MLModel
using theDataSource
and the recipe as information sources.An
MLModel
is nearly immutable. Users can update only theMLModelName
and theScoreThreshold
in anMLModel
without creating a newMLModel
.CreateMLModel
is an asynchronous operation. In response toCreateMLModel
, Amazon Machine Learning (Amazon ML) immediately returns and sets theMLModel
status toPENDING
. After theMLModel
has been created and ready is for use, Amazon ML sets the status toCOMPLETED
.You can use the
GetMLModel
operation to check the progress of theMLModel
during the creation operation.CreateMLModel
requires aDataSource
with computed statistics, which can be created by settingComputeStatistics
totrue
inCreateDataSourceFromRDS
,CreateDataSourceFromS3
, orCreateDataSourceFromRedshift
operations.See also: AWS API Documentation
Request Syntax
response = client.create_ml_model( MLModelId='string', MLModelName='string', MLModelType='REGRESSION'|'BINARY'|'MULTICLASS', Parameters={ 'string': 'string' }, TrainingDataSourceId='string', Recipe='string', RecipeUri='string' )
- Parameters:
MLModelId (string) –
[REQUIRED]
A user-supplied ID that uniquely identifies the
MLModel
.MLModelName (string) – A user-supplied name or description of the
MLModel
.MLModelType (string) –
[REQUIRED]
The category of supervised learning that this
MLModel
will address. Choose from the following types:Choose
REGRESSION
if theMLModel
will be used to predict a numeric value.Choose
BINARY
if theMLModel
result has two possible values.Choose
MULTICLASS
if theMLModel
result has a limited number of values.
For more information, see the Amazon Machine Learning Developer Guide.
Parameters (dict) –
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 from100000
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 the 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 from0
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 from0
toMAX_DOUBLE
. The default is to not use L2 normalization. This parameter can’t be used whenL1
is specified. Use this parameter sparingly.
(string) –
String type.
(string) –
String type.
TrainingDataSourceId (string) –
[REQUIRED]
The
DataSource
that points to the training data.Recipe (string) – The data recipe for creating the
MLModel
. You must specify either the recipe or its URI. If you don’t specify a recipe or its URI, Amazon ML creates a default.RecipeUri (string) – The Amazon Simple Storage Service (Amazon S3) location and file name that contains the
MLModel
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.
- Return type:
dict
- Returns:
Response Syntax
{ 'MLModelId': 'string' }
Response Structure
(dict) –
Represents the output of a
CreateMLModel
operation, and is an acknowledgement that Amazon ML received the request.The
CreateMLModel
operation is asynchronous. You can poll for status updates by using theGetMLModel
operation and checking theStatus
parameter.MLModelId (string) –
A user-supplied ID that uniquely identifies the
MLModel
. This value should be identical to the value of theMLModelId
in the request.
Exceptions
MachineLearning.Client.exceptions.InvalidInputException
MachineLearning.Client.exceptions.InternalServerException
MachineLearning.Client.exceptions.IdempotentParameterMismatchException