class Aws::MachineLearning::Types::MLModel

Represents the output of a `GetMLModel` operation.

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

@!attribute [rw] ml_model_id

The ID assigned to the `MLModel` at creation.
@return [String]

@!attribute [rw] training_data_source_id

The ID of the training `DataSource`. The `CreateMLModel` operation
uses the `TrainingDataSourceId`.
@return [String]

@!attribute [rw] created_by_iam_user

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.
@return [String]

@!attribute [rw] created_at

The time that the `MLModel` was created. The time is expressed in
epoch time.
@return [Time]

@!attribute [rw] last_updated_at

The time of the most recent edit to the `MLModel`. The time is
expressed in epoch time.
@return [Time]

@!attribute [rw] name

A user-supplied name or description of the `MLModel`.
@return [String]

@!attribute [rw] status

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.
@return [String]

@!attribute [rw] size_in_bytes

Long integer type that is a 64-bit signed number.
@return [Integer]

@!attribute [rw] endpoint_info

The current endpoint of the `MLModel`.
@return [Types::RealtimeEndpointInfo]

@!attribute [rw] 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.
@return [Hash<String,String>]

@!attribute [rw] input_data_location_s3

The location of the data file or directory in Amazon Simple Storage
Service (Amazon S3).
@return [String]

@!attribute [rw] 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.

^
@return [String]

@!attribute [rw] ml_model_type

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?".
@return [String]

@!attribute [rw] score_threshold

@return [Float]

@!attribute [rw] score_threshold_last_updated_at

The time of the most recent edit to the `ScoreThreshold`. The time
is expressed in epoch time.
@return [Time]

@!attribute [rw] message

A description of the most recent details about accessing the
`MLModel`.
@return [String]

@!attribute [rw] compute_time

Long integer type that is a 64-bit signed number.
@return [Integer]

@!attribute [rw] finished_at

A timestamp represented in epoch time.
@return [Time]

@!attribute [rw] started_at

A timestamp represented in epoch time.
@return [Time]

Constants

SENSITIVE