machinelearning/model/MLModel.h

/**
 * Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
 * SPDX-License-Identifier: Apache-2.0.
 */

#pragma once
#include <aws/machinelearning/MachineLearning_EXPORTS.h>
#include <aws/core/utils/memory/stl/AWSString.h>
#include <aws/core/utils/DateTime.h>
#include <aws/machinelearning/model/EntityStatus.h>
#include <aws/machinelearning/model/RealtimeEndpointInfo.h>
#include <aws/core/utils/memory/stl/AWSMap.h>
#include <aws/machinelearning/model/Algorithm.h>
#include <aws/machinelearning/model/MLModelType.h>
#include <utility>

namespace Aws
{
namespace Utils
{
namespace Json
{
  class JsonValue;
  class JsonView;
} // namespace Json
} // namespace Utils
namespace MachineLearning
{
namespace Model
{

  /**
   * <p> Represents the output of a <code>GetMLModel</code> operation. </p> <p>The
   * content consists of the detailed metadata and the current status of the
   * <code>MLModel</code>.</p><p><h3>See Also:</h3>   <a
   * href="http://docs.aws.amazon.com/goto/WebAPI/machinelearning-2014-12-12/MLModel">AWS
   * API Reference</a></p>
   */
  class AWS_MACHINELEARNING_API MLModel
  {
  public:
    MLModel();
    MLModel(Aws::Utils::Json::JsonView jsonValue);
    MLModel& operator=(Aws::Utils::Json::JsonView jsonValue);
    Aws::Utils::Json::JsonValue Jsonize() const;


    /**
     * <p>The ID assigned to the <code>MLModel</code> at creation.</p>
     */
    inline const Aws::String& GetMLModelId() const{ return m_mLModelId; }

    /**
     * <p>The ID assigned to the <code>MLModel</code> at creation.</p>
     */
    inline bool MLModelIdHasBeenSet() const { return m_mLModelIdHasBeenSet; }

    /**
     * <p>The ID assigned to the <code>MLModel</code> at creation.</p>
     */
    inline void SetMLModelId(const Aws::String& value) { m_mLModelIdHasBeenSet = true; m_mLModelId = value; }

    /**
     * <p>The ID assigned to the <code>MLModel</code> at creation.</p>
     */
    inline void SetMLModelId(Aws::String&& value) { m_mLModelIdHasBeenSet = true; m_mLModelId = std::move(value); }

    /**
     * <p>The ID assigned to the <code>MLModel</code> at creation.</p>
     */
    inline void SetMLModelId(const char* value) { m_mLModelIdHasBeenSet = true; m_mLModelId.assign(value); }

    /**
     * <p>The ID assigned to the <code>MLModel</code> at creation.</p>
     */
    inline MLModel& WithMLModelId(const Aws::String& value) { SetMLModelId(value); return *this;}

    /**
     * <p>The ID assigned to the <code>MLModel</code> at creation.</p>
     */
    inline MLModel& WithMLModelId(Aws::String&& value) { SetMLModelId(std::move(value)); return *this;}

    /**
     * <p>The ID assigned to the <code>MLModel</code> at creation.</p>
     */
    inline MLModel& WithMLModelId(const char* value) { SetMLModelId(value); return *this;}


    /**
     * <p>The ID of the training <code>DataSource</code>. The
     * <code>CreateMLModel</code> operation uses the
     * <code>TrainingDataSourceId</code>.</p>
     */
    inline const Aws::String& GetTrainingDataSourceId() const{ return m_trainingDataSourceId; }

    /**
     * <p>The ID of the training <code>DataSource</code>. The
     * <code>CreateMLModel</code> operation uses the
     * <code>TrainingDataSourceId</code>.</p>
     */
    inline bool TrainingDataSourceIdHasBeenSet() const { return m_trainingDataSourceIdHasBeenSet; }

    /**
     * <p>The ID of the training <code>DataSource</code>. The
     * <code>CreateMLModel</code> operation uses the
     * <code>TrainingDataSourceId</code>.</p>
     */
    inline void SetTrainingDataSourceId(const Aws::String& value) { m_trainingDataSourceIdHasBeenSet = true; m_trainingDataSourceId = value; }

    /**
     * <p>The ID of the training <code>DataSource</code>. The
     * <code>CreateMLModel</code> operation uses the
     * <code>TrainingDataSourceId</code>.</p>
     */
    inline void SetTrainingDataSourceId(Aws::String&& value) { m_trainingDataSourceIdHasBeenSet = true; m_trainingDataSourceId = std::move(value); }

    /**
     * <p>The ID of the training <code>DataSource</code>. The
     * <code>CreateMLModel</code> operation uses the
     * <code>TrainingDataSourceId</code>.</p>
     */
    inline void SetTrainingDataSourceId(const char* value) { m_trainingDataSourceIdHasBeenSet = true; m_trainingDataSourceId.assign(value); }

    /**
     * <p>The ID of the training <code>DataSource</code>. The
     * <code>CreateMLModel</code> operation uses the
     * <code>TrainingDataSourceId</code>.</p>
     */
    inline MLModel& WithTrainingDataSourceId(const Aws::String& value) { SetTrainingDataSourceId(value); return *this;}

    /**
     * <p>The ID of the training <code>DataSource</code>. The
     * <code>CreateMLModel</code> operation uses the
     * <code>TrainingDataSourceId</code>.</p>
     */
    inline MLModel& WithTrainingDataSourceId(Aws::String&& value) { SetTrainingDataSourceId(std::move(value)); return *this;}

    /**
     * <p>The ID of the training <code>DataSource</code>. The
     * <code>CreateMLModel</code> operation uses the
     * <code>TrainingDataSourceId</code>.</p>
     */
    inline MLModel& WithTrainingDataSourceId(const char* value) { SetTrainingDataSourceId(value); return *this;}


    /**
     * <p>The AWS user account from which the <code>MLModel</code> was created. The
     * account type can be either an AWS root account or an AWS Identity and Access
     * Management (IAM) user account.</p>
     */
    inline const Aws::String& GetCreatedByIamUser() const{ return m_createdByIamUser; }

    /**
     * <p>The AWS user account from which the <code>MLModel</code> was created. The
     * account type can be either an AWS root account or an AWS Identity and Access
     * Management (IAM) user account.</p>
     */
    inline bool CreatedByIamUserHasBeenSet() const { return m_createdByIamUserHasBeenSet; }

    /**
     * <p>The AWS user account from which the <code>MLModel</code> was created. The
     * account type can be either an AWS root account or an AWS Identity and Access
     * Management (IAM) user account.</p>
     */
    inline void SetCreatedByIamUser(const Aws::String& value) { m_createdByIamUserHasBeenSet = true; m_createdByIamUser = value; }

    /**
     * <p>The AWS user account from which the <code>MLModel</code> was created. The
     * account type can be either an AWS root account or an AWS Identity and Access
     * Management (IAM) user account.</p>
     */
    inline void SetCreatedByIamUser(Aws::String&& value) { m_createdByIamUserHasBeenSet = true; m_createdByIamUser = std::move(value); }

    /**
     * <p>The AWS user account from which the <code>MLModel</code> was created. The
     * account type can be either an AWS root account or an AWS Identity and Access
     * Management (IAM) user account.</p>
     */
    inline void SetCreatedByIamUser(const char* value) { m_createdByIamUserHasBeenSet = true; m_createdByIamUser.assign(value); }

    /**
     * <p>The AWS user account from which the <code>MLModel</code> was created. The
     * account type can be either an AWS root account or an AWS Identity and Access
     * Management (IAM) user account.</p>
     */
    inline MLModel& WithCreatedByIamUser(const Aws::String& value) { SetCreatedByIamUser(value); return *this;}

    /**
     * <p>The AWS user account from which the <code>MLModel</code> was created. The
     * account type can be either an AWS root account or an AWS Identity and Access
     * Management (IAM) user account.</p>
     */
    inline MLModel& WithCreatedByIamUser(Aws::String&& value) { SetCreatedByIamUser(std::move(value)); return *this;}

    /**
     * <p>The AWS user account from which the <code>MLModel</code> was created. The
     * account type can be either an AWS root account or an AWS Identity and Access
     * Management (IAM) user account.</p>
     */
    inline MLModel& WithCreatedByIamUser(const char* value) { SetCreatedByIamUser(value); return *this;}


    /**
     * <p>The time that the <code>MLModel</code> was created. The time is expressed in
     * epoch time.</p>
     */
    inline const Aws::Utils::DateTime& GetCreatedAt() const{ return m_createdAt; }

    /**
     * <p>The time that the <code>MLModel</code> was created. The time is expressed in
     * epoch time.</p>
     */
    inline bool CreatedAtHasBeenSet() const { return m_createdAtHasBeenSet; }

    /**
     * <p>The time that the <code>MLModel</code> was created. The time is expressed in
     * epoch time.</p>
     */
    inline void SetCreatedAt(const Aws::Utils::DateTime& value) { m_createdAtHasBeenSet = true; m_createdAt = value; }

    /**
     * <p>The time that the <code>MLModel</code> was created. The time is expressed in
     * epoch time.</p>
     */
    inline void SetCreatedAt(Aws::Utils::DateTime&& value) { m_createdAtHasBeenSet = true; m_createdAt = std::move(value); }

    /**
     * <p>The time that the <code>MLModel</code> was created. The time is expressed in
     * epoch time.</p>
     */
    inline MLModel& WithCreatedAt(const Aws::Utils::DateTime& value) { SetCreatedAt(value); return *this;}

    /**
     * <p>The time that the <code>MLModel</code> was created. The time is expressed in
     * epoch time.</p>
     */
    inline MLModel& WithCreatedAt(Aws::Utils::DateTime&& value) { SetCreatedAt(std::move(value)); return *this;}


    /**
     * <p>The time of the most recent edit to the <code>MLModel</code>. The time is
     * expressed in epoch time.</p>
     */
    inline const Aws::Utils::DateTime& GetLastUpdatedAt() const{ return m_lastUpdatedAt; }

    /**
     * <p>The time of the most recent edit to the <code>MLModel</code>. The time is
     * expressed in epoch time.</p>
     */
    inline bool LastUpdatedAtHasBeenSet() const { return m_lastUpdatedAtHasBeenSet; }

    /**
     * <p>The time of the most recent edit to the <code>MLModel</code>. The time is
     * expressed in epoch time.</p>
     */
    inline void SetLastUpdatedAt(const Aws::Utils::DateTime& value) { m_lastUpdatedAtHasBeenSet = true; m_lastUpdatedAt = value; }

    /**
     * <p>The time of the most recent edit to the <code>MLModel</code>. The time is
     * expressed in epoch time.</p>
     */
    inline void SetLastUpdatedAt(Aws::Utils::DateTime&& value) { m_lastUpdatedAtHasBeenSet = true; m_lastUpdatedAt = std::move(value); }

    /**
     * <p>The time of the most recent edit to the <code>MLModel</code>. The time is
     * expressed in epoch time.</p>
     */
    inline MLModel& WithLastUpdatedAt(const Aws::Utils::DateTime& value) { SetLastUpdatedAt(value); return *this;}

    /**
     * <p>The time of the most recent edit to the <code>MLModel</code>. The time is
     * expressed in epoch time.</p>
     */
    inline MLModel& WithLastUpdatedAt(Aws::Utils::DateTime&& value) { SetLastUpdatedAt(std::move(value)); return *this;}


    /**
     * <p>A user-supplied name or description of the <code>MLModel</code>.</p>
     */
    inline const Aws::String& GetName() const{ return m_name; }

    /**
     * <p>A user-supplied name or description of the <code>MLModel</code>.</p>
     */
    inline bool NameHasBeenSet() const { return m_nameHasBeenSet; }

    /**
     * <p>A user-supplied name or description of the <code>MLModel</code>.</p>
     */
    inline void SetName(const Aws::String& value) { m_nameHasBeenSet = true; m_name = value; }

    /**
     * <p>A user-supplied name or description of the <code>MLModel</code>.</p>
     */
    inline void SetName(Aws::String&& value) { m_nameHasBeenSet = true; m_name = std::move(value); }

    /**
     * <p>A user-supplied name or description of the <code>MLModel</code>.</p>
     */
    inline void SetName(const char* value) { m_nameHasBeenSet = true; m_name.assign(value); }

    /**
     * <p>A user-supplied name or description of the <code>MLModel</code>.</p>
     */
    inline MLModel& WithName(const Aws::String& value) { SetName(value); return *this;}

    /**
     * <p>A user-supplied name or description of the <code>MLModel</code>.</p>
     */
    inline MLModel& WithName(Aws::String&& value) { SetName(std::move(value)); return *this;}

    /**
     * <p>A user-supplied name or description of the <code>MLModel</code>.</p>
     */
    inline MLModel& WithName(const char* value) { SetName(value); return *this;}


    /**
     * <p>The current status of an <code>MLModel</code>. This element can have one of
     * the following values: </p> <ul> <li> <p> <code>PENDING</code> - Amazon Machine
     * Learning (Amazon ML) submitted a request to create an <code>MLModel</code>.</p>
     * </li> <li> <p> <code>INPROGRESS</code> - The creation process is underway.</p>
     * </li> <li> <p> <code>FAILED</code> - The request to create an
     * <code>MLModel</code> didn't run to completion. The model isn't usable.</p> </li>
     * <li> <p> <code>COMPLETED</code> - The creation process completed
     * successfully.</p> </li> <li> <p> <code>DELETED</code> - The <code>MLModel</code>
     * is marked as deleted. It isn't usable.</p> </li> </ul>
     */
    inline const EntityStatus& GetStatus() const{ return m_status; }

    /**
     * <p>The current status of an <code>MLModel</code>. This element can have one of
     * the following values: </p> <ul> <li> <p> <code>PENDING</code> - Amazon Machine
     * Learning (Amazon ML) submitted a request to create an <code>MLModel</code>.</p>
     * </li> <li> <p> <code>INPROGRESS</code> - The creation process is underway.</p>
     * </li> <li> <p> <code>FAILED</code> - The request to create an
     * <code>MLModel</code> didn't run to completion. The model isn't usable.</p> </li>
     * <li> <p> <code>COMPLETED</code> - The creation process completed
     * successfully.</p> </li> <li> <p> <code>DELETED</code> - The <code>MLModel</code>
     * is marked as deleted. It isn't usable.</p> </li> </ul>
     */
    inline bool StatusHasBeenSet() const { return m_statusHasBeenSet; }

    /**
     * <p>The current status of an <code>MLModel</code>. This element can have one of
     * the following values: </p> <ul> <li> <p> <code>PENDING</code> - Amazon Machine
     * Learning (Amazon ML) submitted a request to create an <code>MLModel</code>.</p>
     * </li> <li> <p> <code>INPROGRESS</code> - The creation process is underway.</p>
     * </li> <li> <p> <code>FAILED</code> - The request to create an
     * <code>MLModel</code> didn't run to completion. The model isn't usable.</p> </li>
     * <li> <p> <code>COMPLETED</code> - The creation process completed
     * successfully.</p> </li> <li> <p> <code>DELETED</code> - The <code>MLModel</code>
     * is marked as deleted. It isn't usable.</p> </li> </ul>
     */
    inline void SetStatus(const EntityStatus& value) { m_statusHasBeenSet = true; m_status = value; }

    /**
     * <p>The current status of an <code>MLModel</code>. This element can have one of
     * the following values: </p> <ul> <li> <p> <code>PENDING</code> - Amazon Machine
     * Learning (Amazon ML) submitted a request to create an <code>MLModel</code>.</p>
     * </li> <li> <p> <code>INPROGRESS</code> - The creation process is underway.</p>
     * </li> <li> <p> <code>FAILED</code> - The request to create an
     * <code>MLModel</code> didn't run to completion. The model isn't usable.</p> </li>
     * <li> <p> <code>COMPLETED</code> - The creation process completed
     * successfully.</p> </li> <li> <p> <code>DELETED</code> - The <code>MLModel</code>
     * is marked as deleted. It isn't usable.</p> </li> </ul>
     */
    inline void SetStatus(EntityStatus&& value) { m_statusHasBeenSet = true; m_status = std::move(value); }

    /**
     * <p>The current status of an <code>MLModel</code>. This element can have one of
     * the following values: </p> <ul> <li> <p> <code>PENDING</code> - Amazon Machine
     * Learning (Amazon ML) submitted a request to create an <code>MLModel</code>.</p>
     * </li> <li> <p> <code>INPROGRESS</code> - The creation process is underway.</p>
     * </li> <li> <p> <code>FAILED</code> - The request to create an
     * <code>MLModel</code> didn't run to completion. The model isn't usable.</p> </li>
     * <li> <p> <code>COMPLETED</code> - The creation process completed
     * successfully.</p> </li> <li> <p> <code>DELETED</code> - The <code>MLModel</code>
     * is marked as deleted. It isn't usable.</p> </li> </ul>
     */
    inline MLModel& WithStatus(const EntityStatus& value) { SetStatus(value); return *this;}

    /**
     * <p>The current status of an <code>MLModel</code>. This element can have one of
     * the following values: </p> <ul> <li> <p> <code>PENDING</code> - Amazon Machine
     * Learning (Amazon ML) submitted a request to create an <code>MLModel</code>.</p>
     * </li> <li> <p> <code>INPROGRESS</code> - The creation process is underway.</p>
     * </li> <li> <p> <code>FAILED</code> - The request to create an
     * <code>MLModel</code> didn't run to completion. The model isn't usable.</p> </li>
     * <li> <p> <code>COMPLETED</code> - The creation process completed
     * successfully.</p> </li> <li> <p> <code>DELETED</code> - The <code>MLModel</code>
     * is marked as deleted. It isn't usable.</p> </li> </ul>
     */
    inline MLModel& WithStatus(EntityStatus&& value) { SetStatus(std::move(value)); return *this;}


    inline long long GetSizeInBytes() const{ return m_sizeInBytes; }


    inline bool SizeInBytesHasBeenSet() const { return m_sizeInBytesHasBeenSet; }


    inline void SetSizeInBytes(long long value) { m_sizeInBytesHasBeenSet = true; m_sizeInBytes = value; }


    inline MLModel& WithSizeInBytes(long long value) { SetSizeInBytes(value); return *this;}


    /**
     * <p>The current endpoint of the <code>MLModel</code>.</p>
     */
    inline const RealtimeEndpointInfo& GetEndpointInfo() const{ return m_endpointInfo; }

    /**
     * <p>The current endpoint of the <code>MLModel</code>.</p>
     */
    inline bool EndpointInfoHasBeenSet() const { return m_endpointInfoHasBeenSet; }

    /**
     * <p>The current endpoint of the <code>MLModel</code>.</p>
     */
    inline void SetEndpointInfo(const RealtimeEndpointInfo& value) { m_endpointInfoHasBeenSet = true; m_endpointInfo = value; }

    /**
     * <p>The current endpoint of the <code>MLModel</code>.</p>
     */
    inline void SetEndpointInfo(RealtimeEndpointInfo&& value) { m_endpointInfoHasBeenSet = true; m_endpointInfo = std::move(value); }

    /**
     * <p>The current endpoint of the <code>MLModel</code>.</p>
     */
    inline MLModel& WithEndpointInfo(const RealtimeEndpointInfo& value) { SetEndpointInfo(value); return *this;}

    /**
     * <p>The current endpoint of the <code>MLModel</code>.</p>
     */
    inline MLModel& WithEndpointInfo(RealtimeEndpointInfo&& value) { SetEndpointInfo(std::move(value)); return *this;}


    /**
     * <p>A list of the training parameters in the <code>MLModel</code>. The list is
     * implemented as a map of key-value pairs.</p> <p>The following is the current set
     * of training parameters:</p> <ul> <li> <p> <code>sgd.maxMLModelSizeInBytes</code>
     * - The maximum allowed size of the model. Depending on the input data, the size
     * of the model might affect its performance.</p> <p> The value is an integer that
     * ranges from <code>100000</code> to <code>2147483648</code>. The default value is
     * <code>33554432</code>.</p> </li> <li> <p> <code>sgd.maxPasses</code> - The
     * number of times that the training process traverses the observations to build
     * the <code>MLModel</code>. The value is an integer that ranges from
     * <code>1</code> to <code>10000</code>. The default value is <code>10</code>.</p>
     * </li> <li> <p> <code>sgd.shuffleType</code> - 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 <code>auto</code> and
     * <code>none</code>. The default value is <code>none</code>.</p> </li> <li> <p>
     * <code>sgd.l1RegularizationAmount</code> - 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
     * <code>1.0E-08</code>.</p> <p>The value is a double that ranges from
     * <code>0</code> to <code>MAX_DOUBLE</code>. The default is to not use L1
     * normalization. This parameter can't be used when <code>L2</code> is specified.
     * Use this parameter sparingly.</p> </li> <li> <p>
     * <code>sgd.l2RegularizationAmount</code> - 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 <code>1.0E-08</code>.</p> <p>The
     * value is a double that ranges from <code>0</code> to <code>MAX_DOUBLE</code>.
     * The default is to not use L2 normalization. This parameter can't be used when
     * <code>L1</code> is specified. Use this parameter sparingly.</p> </li> </ul>
     */
    inline const Aws::Map<Aws::String, Aws::String>& GetTrainingParameters() const{ return m_trainingParameters; }

    /**
     * <p>A list of the training parameters in the <code>MLModel</code>. The list is
     * implemented as a map of key-value pairs.</p> <p>The following is the current set
     * of training parameters:</p> <ul> <li> <p> <code>sgd.maxMLModelSizeInBytes</code>
     * - The maximum allowed size of the model. Depending on the input data, the size
     * of the model might affect its performance.</p> <p> The value is an integer that
     * ranges from <code>100000</code> to <code>2147483648</code>. The default value is
     * <code>33554432</code>.</p> </li> <li> <p> <code>sgd.maxPasses</code> - The
     * number of times that the training process traverses the observations to build
     * the <code>MLModel</code>. The value is an integer that ranges from
     * <code>1</code> to <code>10000</code>. The default value is <code>10</code>.</p>
     * </li> <li> <p> <code>sgd.shuffleType</code> - 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 <code>auto</code> and
     * <code>none</code>. The default value is <code>none</code>.</p> </li> <li> <p>
     * <code>sgd.l1RegularizationAmount</code> - 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
     * <code>1.0E-08</code>.</p> <p>The value is a double that ranges from
     * <code>0</code> to <code>MAX_DOUBLE</code>. The default is to not use L1
     * normalization. This parameter can't be used when <code>L2</code> is specified.
     * Use this parameter sparingly.</p> </li> <li> <p>
     * <code>sgd.l2RegularizationAmount</code> - 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 <code>1.0E-08</code>.</p> <p>The
     * value is a double that ranges from <code>0</code> to <code>MAX_DOUBLE</code>.
     * The default is to not use L2 normalization. This parameter can't be used when
     * <code>L1</code> is specified. Use this parameter sparingly.</p> </li> </ul>
     */
    inline bool TrainingParametersHasBeenSet() const { return m_trainingParametersHasBeenSet; }

    /**
     * <p>A list of the training parameters in the <code>MLModel</code>. The list is
     * implemented as a map of key-value pairs.</p> <p>The following is the current set
     * of training parameters:</p> <ul> <li> <p> <code>sgd.maxMLModelSizeInBytes</code>
     * - The maximum allowed size of the model. Depending on the input data, the size
     * of the model might affect its performance.</p> <p> The value is an integer that
     * ranges from <code>100000</code> to <code>2147483648</code>. The default value is
     * <code>33554432</code>.</p> </li> <li> <p> <code>sgd.maxPasses</code> - The
     * number of times that the training process traverses the observations to build
     * the <code>MLModel</code>. The value is an integer that ranges from
     * <code>1</code> to <code>10000</code>. The default value is <code>10</code>.</p>
     * </li> <li> <p> <code>sgd.shuffleType</code> - 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 <code>auto</code> and
     * <code>none</code>. The default value is <code>none</code>.</p> </li> <li> <p>
     * <code>sgd.l1RegularizationAmount</code> - 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
     * <code>1.0E-08</code>.</p> <p>The value is a double that ranges from
     * <code>0</code> to <code>MAX_DOUBLE</code>. The default is to not use L1
     * normalization. This parameter can't be used when <code>L2</code> is specified.
     * Use this parameter sparingly.</p> </li> <li> <p>
     * <code>sgd.l2RegularizationAmount</code> - 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 <code>1.0E-08</code>.</p> <p>The
     * value is a double that ranges from <code>0</code> to <code>MAX_DOUBLE</code>.
     * The default is to not use L2 normalization. This parameter can't be used when
     * <code>L1</code> is specified. Use this parameter sparingly.</p> </li> </ul>
     */
    inline void SetTrainingParameters(const Aws::Map<Aws::String, Aws::String>& value) { m_trainingParametersHasBeenSet = true; m_trainingParameters = value; }

    /**
     * <p>A list of the training parameters in the <code>MLModel</code>. The list is
     * implemented as a map of key-value pairs.</p> <p>The following is the current set
     * of training parameters:</p> <ul> <li> <p> <code>sgd.maxMLModelSizeInBytes</code>
     * - The maximum allowed size of the model. Depending on the input data, the size
     * of the model might affect its performance.</p> <p> The value is an integer that
     * ranges from <code>100000</code> to <code>2147483648</code>. The default value is
     * <code>33554432</code>.</p> </li> <li> <p> <code>sgd.maxPasses</code> - The
     * number of times that the training process traverses the observations to build
     * the <code>MLModel</code>. The value is an integer that ranges from
     * <code>1</code> to <code>10000</code>. The default value is <code>10</code>.</p>
     * </li> <li> <p> <code>sgd.shuffleType</code> - 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 <code>auto</code> and
     * <code>none</code>. The default value is <code>none</code>.</p> </li> <li> <p>
     * <code>sgd.l1RegularizationAmount</code> - 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
     * <code>1.0E-08</code>.</p> <p>The value is a double that ranges from
     * <code>0</code> to <code>MAX_DOUBLE</code>. The default is to not use L1
     * normalization. This parameter can't be used when <code>L2</code> is specified.
     * Use this parameter sparingly.</p> </li> <li> <p>
     * <code>sgd.l2RegularizationAmount</code> - 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 <code>1.0E-08</code>.</p> <p>The
     * value is a double that ranges from <code>0</code> to <code>MAX_DOUBLE</code>.
     * The default is to not use L2 normalization. This parameter can't be used when
     * <code>L1</code> is specified. Use this parameter sparingly.</p> </li> </ul>
     */
    inline void SetTrainingParameters(Aws::Map<Aws::String, Aws::String>&& value) { m_trainingParametersHasBeenSet = true; m_trainingParameters = std::move(value); }

    /**
     * <p>A list of the training parameters in the <code>MLModel</code>. The list is
     * implemented as a map of key-value pairs.</p> <p>The following is the current set
     * of training parameters:</p> <ul> <li> <p> <code>sgd.maxMLModelSizeInBytes</code>
     * - The maximum allowed size of the model. Depending on the input data, the size
     * of the model might affect its performance.</p> <p> The value is an integer that
     * ranges from <code>100000</code> to <code>2147483648</code>. The default value is
     * <code>33554432</code>.</p> </li> <li> <p> <code>sgd.maxPasses</code> - The
     * number of times that the training process traverses the observations to build
     * the <code>MLModel</code>. The value is an integer that ranges from
     * <code>1</code> to <code>10000</code>. The default value is <code>10</code>.</p>
     * </li> <li> <p> <code>sgd.shuffleType</code> - 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 <code>auto</code> and
     * <code>none</code>. The default value is <code>none</code>.</p> </li> <li> <p>
     * <code>sgd.l1RegularizationAmount</code> - 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
     * <code>1.0E-08</code>.</p> <p>The value is a double that ranges from
     * <code>0</code> to <code>MAX_DOUBLE</code>. The default is to not use L1
     * normalization. This parameter can't be used when <code>L2</code> is specified.
     * Use this parameter sparingly.</p> </li> <li> <p>
     * <code>sgd.l2RegularizationAmount</code> - 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 <code>1.0E-08</code>.</p> <p>The
     * value is a double that ranges from <code>0</code> to <code>MAX_DOUBLE</code>.
     * The default is to not use L2 normalization. This parameter can't be used when
     * <code>L1</code> is specified. Use this parameter sparingly.</p> </li> </ul>
     */
    inline MLModel& WithTrainingParameters(const Aws::Map<Aws::String, Aws::String>& value) { SetTrainingParameters(value); return *this;}

    /**
     * <p>A list of the training parameters in the <code>MLModel</code>. The list is
     * implemented as a map of key-value pairs.</p> <p>The following is the current set
     * of training parameters:</p> <ul> <li> <p> <code>sgd.maxMLModelSizeInBytes</code>
     * - The maximum allowed size of the model. Depending on the input data, the size
     * of the model might affect its performance.</p> <p> The value is an integer that
     * ranges from <code>100000</code> to <code>2147483648</code>. The default value is
     * <code>33554432</code>.</p> </li> <li> <p> <code>sgd.maxPasses</code> - The
     * number of times that the training process traverses the observations to build
     * the <code>MLModel</code>. The value is an integer that ranges from
     * <code>1</code> to <code>10000</code>. The default value is <code>10</code>.</p>
     * </li> <li> <p> <code>sgd.shuffleType</code> - 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 <code>auto</code> and
     * <code>none</code>. The default value is <code>none</code>.</p> </li> <li> <p>
     * <code>sgd.l1RegularizationAmount</code> - 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
     * <code>1.0E-08</code>.</p> <p>The value is a double that ranges from
     * <code>0</code> to <code>MAX_DOUBLE</code>. The default is to not use L1
     * normalization. This parameter can't be used when <code>L2</code> is specified.
     * Use this parameter sparingly.</p> </li> <li> <p>
     * <code>sgd.l2RegularizationAmount</code> - 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 <code>1.0E-08</code>.</p> <p>The
     * value is a double that ranges from <code>0</code> to <code>MAX_DOUBLE</code>.
     * The default is to not use L2 normalization. This parameter can't be used when
     * <code>L1</code> is specified. Use this parameter sparingly.</p> </li> </ul>
     */
    inline MLModel& WithTrainingParameters(Aws::Map<Aws::String, Aws::String>&& value) { SetTrainingParameters(std::move(value)); return *this;}

    /**
     * <p>A list of the training parameters in the <code>MLModel</code>. The list is
     * implemented as a map of key-value pairs.</p> <p>The following is the current set
     * of training parameters:</p> <ul> <li> <p> <code>sgd.maxMLModelSizeInBytes</code>
     * - The maximum allowed size of the model. Depending on the input data, the size
     * of the model might affect its performance.</p> <p> The value is an integer that
     * ranges from <code>100000</code> to <code>2147483648</code>. The default value is
     * <code>33554432</code>.</p> </li> <li> <p> <code>sgd.maxPasses</code> - The
     * number of times that the training process traverses the observations to build
     * the <code>MLModel</code>. The value is an integer that ranges from
     * <code>1</code> to <code>10000</code>. The default value is <code>10</code>.</p>
     * </li> <li> <p> <code>sgd.shuffleType</code> - 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 <code>auto</code> and
     * <code>none</code>. The default value is <code>none</code>.</p> </li> <li> <p>
     * <code>sgd.l1RegularizationAmount</code> - 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
     * <code>1.0E-08</code>.</p> <p>The value is a double that ranges from
     * <code>0</code> to <code>MAX_DOUBLE</code>. The default is to not use L1
     * normalization. This parameter can't be used when <code>L2</code> is specified.
     * Use this parameter sparingly.</p> </li> <li> <p>
     * <code>sgd.l2RegularizationAmount</code> - 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 <code>1.0E-08</code>.</p> <p>The
     * value is a double that ranges from <code>0</code> to <code>MAX_DOUBLE</code>.
     * The default is to not use L2 normalization. This parameter can't be used when
     * <code>L1</code> is specified. Use this parameter sparingly.</p> </li> </ul>
     */
    inline MLModel& AddTrainingParameters(const Aws::String& key, const Aws::String& value) { m_trainingParametersHasBeenSet = true; m_trainingParameters.emplace(key, value); return *this; }

    /**
     * <p>A list of the training parameters in the <code>MLModel</code>. The list is
     * implemented as a map of key-value pairs.</p> <p>The following is the current set
     * of training parameters:</p> <ul> <li> <p> <code>sgd.maxMLModelSizeInBytes</code>
     * - The maximum allowed size of the model. Depending on the input data, the size
     * of the model might affect its performance.</p> <p> The value is an integer that
     * ranges from <code>100000</code> to <code>2147483648</code>. The default value is
     * <code>33554432</code>.</p> </li> <li> <p> <code>sgd.maxPasses</code> - The
     * number of times that the training process traverses the observations to build
     * the <code>MLModel</code>. The value is an integer that ranges from
     * <code>1</code> to <code>10000</code>. The default value is <code>10</code>.</p>
     * </li> <li> <p> <code>sgd.shuffleType</code> - 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 <code>auto</code> and
     * <code>none</code>. The default value is <code>none</code>.</p> </li> <li> <p>
     * <code>sgd.l1RegularizationAmount</code> - 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
     * <code>1.0E-08</code>.</p> <p>The value is a double that ranges from
     * <code>0</code> to <code>MAX_DOUBLE</code>. The default is to not use L1
     * normalization. This parameter can't be used when <code>L2</code> is specified.
     * Use this parameter sparingly.</p> </li> <li> <p>
     * <code>sgd.l2RegularizationAmount</code> - 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 <code>1.0E-08</code>.</p> <p>The
     * value is a double that ranges from <code>0</code> to <code>MAX_DOUBLE</code>.
     * The default is to not use L2 normalization. This parameter can't be used when
     * <code>L1</code> is specified. Use this parameter sparingly.</p> </li> </ul>
     */
    inline MLModel& AddTrainingParameters(Aws::String&& key, const Aws::String& value) { m_trainingParametersHasBeenSet = true; m_trainingParameters.emplace(std::move(key), value); return *this; }

    /**
     * <p>A list of the training parameters in the <code>MLModel</code>. The list is
     * implemented as a map of key-value pairs.</p> <p>The following is the current set
     * of training parameters:</p> <ul> <li> <p> <code>sgd.maxMLModelSizeInBytes</code>
     * - The maximum allowed size of the model. Depending on the input data, the size
     * of the model might affect its performance.</p> <p> The value is an integer that
     * ranges from <code>100000</code> to <code>2147483648</code>. The default value is
     * <code>33554432</code>.</p> </li> <li> <p> <code>sgd.maxPasses</code> - The
     * number of times that the training process traverses the observations to build
     * the <code>MLModel</code>. The value is an integer that ranges from
     * <code>1</code> to <code>10000</code>. The default value is <code>10</code>.</p>
     * </li> <li> <p> <code>sgd.shuffleType</code> - 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 <code>auto</code> and
     * <code>none</code>. The default value is <code>none</code>.</p> </li> <li> <p>
     * <code>sgd.l1RegularizationAmount</code> - 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
     * <code>1.0E-08</code>.</p> <p>The value is a double that ranges from
     * <code>0</code> to <code>MAX_DOUBLE</code>. The default is to not use L1
     * normalization. This parameter can't be used when <code>L2</code> is specified.
     * Use this parameter sparingly.</p> </li> <li> <p>
     * <code>sgd.l2RegularizationAmount</code> - 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 <code>1.0E-08</code>.</p> <p>The
     * value is a double that ranges from <code>0</code> to <code>MAX_DOUBLE</code>.
     * The default is to not use L2 normalization. This parameter can't be used when
     * <code>L1</code> is specified. Use this parameter sparingly.</p> </li> </ul>
     */
    inline MLModel& AddTrainingParameters(const Aws::String& key, Aws::String&& value) { m_trainingParametersHasBeenSet = true; m_trainingParameters.emplace(key, std::move(value)); return *this; }

    /**
     * <p>A list of the training parameters in the <code>MLModel</code>. The list is
     * implemented as a map of key-value pairs.</p> <p>The following is the current set
     * of training parameters:</p> <ul> <li> <p> <code>sgd.maxMLModelSizeInBytes</code>
     * - The maximum allowed size of the model. Depending on the input data, the size
     * of the model might affect its performance.</p> <p> The value is an integer that
     * ranges from <code>100000</code> to <code>2147483648</code>. The default value is
     * <code>33554432</code>.</p> </li> <li> <p> <code>sgd.maxPasses</code> - The
     * number of times that the training process traverses the observations to build
     * the <code>MLModel</code>. The value is an integer that ranges from
     * <code>1</code> to <code>10000</code>. The default value is <code>10</code>.</p>
     * </li> <li> <p> <code>sgd.shuffleType</code> - 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 <code>auto</code> and
     * <code>none</code>. The default value is <code>none</code>.</p> </li> <li> <p>
     * <code>sgd.l1RegularizationAmount</code> - 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
     * <code>1.0E-08</code>.</p> <p>The value is a double that ranges from
     * <code>0</code> to <code>MAX_DOUBLE</code>. The default is to not use L1
     * normalization. This parameter can't be used when <code>L2</code> is specified.
     * Use this parameter sparingly.</p> </li> <li> <p>
     * <code>sgd.l2RegularizationAmount</code> - 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 <code>1.0E-08</code>.</p> <p>The
     * value is a double that ranges from <code>0</code> to <code>MAX_DOUBLE</code>.
     * The default is to not use L2 normalization. This parameter can't be used when
     * <code>L1</code> is specified. Use this parameter sparingly.</p> </li> </ul>
     */
    inline MLModel& AddTrainingParameters(Aws::String&& key, Aws::String&& value) { m_trainingParametersHasBeenSet = true; m_trainingParameters.emplace(std::move(key), std::move(value)); return *this; }

    /**
     * <p>A list of the training parameters in the <code>MLModel</code>. The list is
     * implemented as a map of key-value pairs.</p> <p>The following is the current set
     * of training parameters:</p> <ul> <li> <p> <code>sgd.maxMLModelSizeInBytes</code>
     * - The maximum allowed size of the model. Depending on the input data, the size
     * of the model might affect its performance.</p> <p> The value is an integer that
     * ranges from <code>100000</code> to <code>2147483648</code>. The default value is
     * <code>33554432</code>.</p> </li> <li> <p> <code>sgd.maxPasses</code> - The
     * number of times that the training process traverses the observations to build
     * the <code>MLModel</code>. The value is an integer that ranges from
     * <code>1</code> to <code>10000</code>. The default value is <code>10</code>.</p>
     * </li> <li> <p> <code>sgd.shuffleType</code> - 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 <code>auto</code> and
     * <code>none</code>. The default value is <code>none</code>.</p> </li> <li> <p>
     * <code>sgd.l1RegularizationAmount</code> - 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
     * <code>1.0E-08</code>.</p> <p>The value is a double that ranges from
     * <code>0</code> to <code>MAX_DOUBLE</code>. The default is to not use L1
     * normalization. This parameter can't be used when <code>L2</code> is specified.
     * Use this parameter sparingly.</p> </li> <li> <p>
     * <code>sgd.l2RegularizationAmount</code> - 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 <code>1.0E-08</code>.</p> <p>The
     * value is a double that ranges from <code>0</code> to <code>MAX_DOUBLE</code>.
     * The default is to not use L2 normalization. This parameter can't be used when
     * <code>L1</code> is specified. Use this parameter sparingly.</p> </li> </ul>
     */
    inline MLModel& AddTrainingParameters(const char* key, Aws::String&& value) { m_trainingParametersHasBeenSet = true; m_trainingParameters.emplace(key, std::move(value)); return *this; }

    /**
     * <p>A list of the training parameters in the <code>MLModel</code>. The list is
     * implemented as a map of key-value pairs.</p> <p>The following is the current set
     * of training parameters:</p> <ul> <li> <p> <code>sgd.maxMLModelSizeInBytes</code>
     * - The maximum allowed size of the model. Depending on the input data, the size
     * of the model might affect its performance.</p> <p> The value is an integer that
     * ranges from <code>100000</code> to <code>2147483648</code>. The default value is
     * <code>33554432</code>.</p> </li> <li> <p> <code>sgd.maxPasses</code> - The
     * number of times that the training process traverses the observations to build
     * the <code>MLModel</code>. The value is an integer that ranges from
     * <code>1</code> to <code>10000</code>. The default value is <code>10</code>.</p>
     * </li> <li> <p> <code>sgd.shuffleType</code> - 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 <code>auto</code> and
     * <code>none</code>. The default value is <code>none</code>.</p> </li> <li> <p>
     * <code>sgd.l1RegularizationAmount</code> - 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
     * <code>1.0E-08</code>.</p> <p>The value is a double that ranges from
     * <code>0</code> to <code>MAX_DOUBLE</code>. The default is to not use L1
     * normalization. This parameter can't be used when <code>L2</code> is specified.
     * Use this parameter sparingly.</p> </li> <li> <p>
     * <code>sgd.l2RegularizationAmount</code> - 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 <code>1.0E-08</code>.</p> <p>The
     * value is a double that ranges from <code>0</code> to <code>MAX_DOUBLE</code>.
     * The default is to not use L2 normalization. This parameter can't be used when
     * <code>L1</code> is specified. Use this parameter sparingly.</p> </li> </ul>
     */
    inline MLModel& AddTrainingParameters(Aws::String&& key, const char* value) { m_trainingParametersHasBeenSet = true; m_trainingParameters.emplace(std::move(key), value); return *this; }

    /**
     * <p>A list of the training parameters in the <code>MLModel</code>. The list is
     * implemented as a map of key-value pairs.</p> <p>The following is the current set
     * of training parameters:</p> <ul> <li> <p> <code>sgd.maxMLModelSizeInBytes</code>
     * - The maximum allowed size of the model. Depending on the input data, the size
     * of the model might affect its performance.</p> <p> The value is an integer that
     * ranges from <code>100000</code> to <code>2147483648</code>. The default value is
     * <code>33554432</code>.</p> </li> <li> <p> <code>sgd.maxPasses</code> - The
     * number of times that the training process traverses the observations to build
     * the <code>MLModel</code>. The value is an integer that ranges from
     * <code>1</code> to <code>10000</code>. The default value is <code>10</code>.</p>
     * </li> <li> <p> <code>sgd.shuffleType</code> - 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 <code>auto</code> and
     * <code>none</code>. The default value is <code>none</code>.</p> </li> <li> <p>
     * <code>sgd.l1RegularizationAmount</code> - 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
     * <code>1.0E-08</code>.</p> <p>The value is a double that ranges from
     * <code>0</code> to <code>MAX_DOUBLE</code>. The default is to not use L1
     * normalization. This parameter can't be used when <code>L2</code> is specified.
     * Use this parameter sparingly.</p> </li> <li> <p>
     * <code>sgd.l2RegularizationAmount</code> - 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 <code>1.0E-08</code>.</p> <p>The
     * value is a double that ranges from <code>0</code> to <code>MAX_DOUBLE</code>.
     * The default is to not use L2 normalization. This parameter can't be used when
     * <code>L1</code> is specified. Use this parameter sparingly.</p> </li> </ul>
     */
    inline MLModel& AddTrainingParameters(const char* key, const char* value) { m_trainingParametersHasBeenSet = true; m_trainingParameters.emplace(key, value); return *this; }


    /**
     * <p>The location of the data file or directory in Amazon Simple Storage Service
     * (Amazon S3).</p>
     */
    inline const Aws::String& GetInputDataLocationS3() const{ return m_inputDataLocationS3; }

    /**
     * <p>The location of the data file or directory in Amazon Simple Storage Service
     * (Amazon S3).</p>
     */
    inline bool InputDataLocationS3HasBeenSet() const { return m_inputDataLocationS3HasBeenSet; }

    /**
     * <p>The location of the data file or directory in Amazon Simple Storage Service
     * (Amazon S3).</p>
     */
    inline void SetInputDataLocationS3(const Aws::String& value) { m_inputDataLocationS3HasBeenSet = true; m_inputDataLocationS3 = value; }

    /**
     * <p>The location of the data file or directory in Amazon Simple Storage Service
     * (Amazon S3).</p>
     */
    inline void SetInputDataLocationS3(Aws::String&& value) { m_inputDataLocationS3HasBeenSet = true; m_inputDataLocationS3 = std::move(value); }

    /**
     * <p>The location of the data file or directory in Amazon Simple Storage Service
     * (Amazon S3).</p>
     */
    inline void SetInputDataLocationS3(const char* value) { m_inputDataLocationS3HasBeenSet = true; m_inputDataLocationS3.assign(value); }

    /**
     * <p>The location of the data file or directory in Amazon Simple Storage Service
     * (Amazon S3).</p>
     */
    inline MLModel& WithInputDataLocationS3(const Aws::String& value) { SetInputDataLocationS3(value); return *this;}

    /**
     * <p>The location of the data file or directory in Amazon Simple Storage Service
     * (Amazon S3).</p>
     */
    inline MLModel& WithInputDataLocationS3(Aws::String&& value) { SetInputDataLocationS3(std::move(value)); return *this;}

    /**
     * <p>The location of the data file or directory in Amazon Simple Storage Service
     * (Amazon S3).</p>
     */
    inline MLModel& WithInputDataLocationS3(const char* value) { SetInputDataLocationS3(value); return *this;}


    /**
     * <p>The algorithm used to train the <code>MLModel</code>. The following algorithm
     * is supported:</p> <ul> <li> <p> <code>SGD</code> -- Stochastic gradient descent.
     * The goal of <code>SGD</code> is to minimize the gradient of the loss function.
     * </p> </li> </ul>
     */
    inline const Algorithm& GetAlgorithm() const{ return m_algorithm; }

    /**
     * <p>The algorithm used to train the <code>MLModel</code>. The following algorithm
     * is supported:</p> <ul> <li> <p> <code>SGD</code> -- Stochastic gradient descent.
     * The goal of <code>SGD</code> is to minimize the gradient of the loss function.
     * </p> </li> </ul>
     */
    inline bool AlgorithmHasBeenSet() const { return m_algorithmHasBeenSet; }

    /**
     * <p>The algorithm used to train the <code>MLModel</code>. The following algorithm
     * is supported:</p> <ul> <li> <p> <code>SGD</code> -- Stochastic gradient descent.
     * The goal of <code>SGD</code> is to minimize the gradient of the loss function.
     * </p> </li> </ul>
     */
    inline void SetAlgorithm(const Algorithm& value) { m_algorithmHasBeenSet = true; m_algorithm = value; }

    /**
     * <p>The algorithm used to train the <code>MLModel</code>. The following algorithm
     * is supported:</p> <ul> <li> <p> <code>SGD</code> -- Stochastic gradient descent.
     * The goal of <code>SGD</code> is to minimize the gradient of the loss function.
     * </p> </li> </ul>
     */
    inline void SetAlgorithm(Algorithm&& value) { m_algorithmHasBeenSet = true; m_algorithm = std::move(value); }

    /**
     * <p>The algorithm used to train the <code>MLModel</code>. The following algorithm
     * is supported:</p> <ul> <li> <p> <code>SGD</code> -- Stochastic gradient descent.
     * The goal of <code>SGD</code> is to minimize the gradient of the loss function.
     * </p> </li> </ul>
     */
    inline MLModel& WithAlgorithm(const Algorithm& value) { SetAlgorithm(value); return *this;}

    /**
     * <p>The algorithm used to train the <code>MLModel</code>. The following algorithm
     * is supported:</p> <ul> <li> <p> <code>SGD</code> -- Stochastic gradient descent.
     * The goal of <code>SGD</code> is to minimize the gradient of the loss function.
     * </p> </li> </ul>
     */
    inline MLModel& WithAlgorithm(Algorithm&& value) { SetAlgorithm(std::move(value)); return *this;}


    /**
     * <p>Identifies the <code>MLModel</code> category. The following are the available
     * types:</p> <ul> <li> <p> <code>REGRESSION</code> - Produces a numeric result.
     * For example, "What price should a house be listed at?"</p> </li> <li> <p>
     * <code>BINARY</code> - Produces one of two possible results. For example, "Is
     * this a child-friendly web site?".</p> </li> <li> <p> <code>MULTICLASS</code> -
     * Produces one of several possible results. For example, "Is this a HIGH-, LOW-,
     * or MEDIUM-risk trade?".</p> </li> </ul>
     */
    inline const MLModelType& GetMLModelType() const{ return m_mLModelType; }

    /**
     * <p>Identifies the <code>MLModel</code> category. The following are the available
     * types:</p> <ul> <li> <p> <code>REGRESSION</code> - Produces a numeric result.
     * For example, "What price should a house be listed at?"</p> </li> <li> <p>
     * <code>BINARY</code> - Produces one of two possible results. For example, "Is
     * this a child-friendly web site?".</p> </li> <li> <p> <code>MULTICLASS</code> -
     * Produces one of several possible results. For example, "Is this a HIGH-, LOW-,
     * or MEDIUM-risk trade?".</p> </li> </ul>
     */
    inline bool MLModelTypeHasBeenSet() const { return m_mLModelTypeHasBeenSet; }

    /**
     * <p>Identifies the <code>MLModel</code> category. The following are the available
     * types:</p> <ul> <li> <p> <code>REGRESSION</code> - Produces a numeric result.
     * For example, "What price should a house be listed at?"</p> </li> <li> <p>
     * <code>BINARY</code> - Produces one of two possible results. For example, "Is
     * this a child-friendly web site?".</p> </li> <li> <p> <code>MULTICLASS</code> -
     * Produces one of several possible results. For example, "Is this a HIGH-, LOW-,
     * or MEDIUM-risk trade?".</p> </li> </ul>
     */
    inline void SetMLModelType(const MLModelType& value) { m_mLModelTypeHasBeenSet = true; m_mLModelType = value; }

    /**
     * <p>Identifies the <code>MLModel</code> category. The following are the available
     * types:</p> <ul> <li> <p> <code>REGRESSION</code> - Produces a numeric result.
     * For example, "What price should a house be listed at?"</p> </li> <li> <p>
     * <code>BINARY</code> - Produces one of two possible results. For example, "Is
     * this a child-friendly web site?".</p> </li> <li> <p> <code>MULTICLASS</code> -
     * Produces one of several possible results. For example, "Is this a HIGH-, LOW-,
     * or MEDIUM-risk trade?".</p> </li> </ul>
     */
    inline void SetMLModelType(MLModelType&& value) { m_mLModelTypeHasBeenSet = true; m_mLModelType = std::move(value); }

    /**
     * <p>Identifies the <code>MLModel</code> category. The following are the available
     * types:</p> <ul> <li> <p> <code>REGRESSION</code> - Produces a numeric result.
     * For example, "What price should a house be listed at?"</p> </li> <li> <p>
     * <code>BINARY</code> - Produces one of two possible results. For example, "Is
     * this a child-friendly web site?".</p> </li> <li> <p> <code>MULTICLASS</code> -
     * Produces one of several possible results. For example, "Is this a HIGH-, LOW-,
     * or MEDIUM-risk trade?".</p> </li> </ul>
     */
    inline MLModel& WithMLModelType(const MLModelType& value) { SetMLModelType(value); return *this;}

    /**
     * <p>Identifies the <code>MLModel</code> category. The following are the available
     * types:</p> <ul> <li> <p> <code>REGRESSION</code> - Produces a numeric result.
     * For example, "What price should a house be listed at?"</p> </li> <li> <p>
     * <code>BINARY</code> - Produces one of two possible results. For example, "Is
     * this a child-friendly web site?".</p> </li> <li> <p> <code>MULTICLASS</code> -
     * Produces one of several possible results. For example, "Is this a HIGH-, LOW-,
     * or MEDIUM-risk trade?".</p> </li> </ul>
     */
    inline MLModel& WithMLModelType(MLModelType&& value) { SetMLModelType(std::move(value)); return *this;}


    inline double GetScoreThreshold() const{ return m_scoreThreshold; }


    inline bool ScoreThresholdHasBeenSet() const { return m_scoreThresholdHasBeenSet; }


    inline void SetScoreThreshold(double value) { m_scoreThresholdHasBeenSet = true; m_scoreThreshold = value; }


    inline MLModel& WithScoreThreshold(double value) { SetScoreThreshold(value); return *this;}


    /**
     * <p>The time of the most recent edit to the <code>ScoreThreshold</code>. The time
     * is expressed in epoch time.</p>
     */
    inline const Aws::Utils::DateTime& GetScoreThresholdLastUpdatedAt() const{ return m_scoreThresholdLastUpdatedAt; }

    /**
     * <p>The time of the most recent edit to the <code>ScoreThreshold</code>. The time
     * is expressed in epoch time.</p>
     */
    inline bool ScoreThresholdLastUpdatedAtHasBeenSet() const { return m_scoreThresholdLastUpdatedAtHasBeenSet; }

    /**
     * <p>The time of the most recent edit to the <code>ScoreThreshold</code>. The time
     * is expressed in epoch time.</p>
     */
    inline void SetScoreThresholdLastUpdatedAt(const Aws::Utils::DateTime& value) { m_scoreThresholdLastUpdatedAtHasBeenSet = true; m_scoreThresholdLastUpdatedAt = value; }

    /**
     * <p>The time of the most recent edit to the <code>ScoreThreshold</code>. The time
     * is expressed in epoch time.</p>
     */
    inline void SetScoreThresholdLastUpdatedAt(Aws::Utils::DateTime&& value) { m_scoreThresholdLastUpdatedAtHasBeenSet = true; m_scoreThresholdLastUpdatedAt = std::move(value); }

    /**
     * <p>The time of the most recent edit to the <code>ScoreThreshold</code>. The time
     * is expressed in epoch time.</p>
     */
    inline MLModel& WithScoreThresholdLastUpdatedAt(const Aws::Utils::DateTime& value) { SetScoreThresholdLastUpdatedAt(value); return *this;}

    /**
     * <p>The time of the most recent edit to the <code>ScoreThreshold</code>. The time
     * is expressed in epoch time.</p>
     */
    inline MLModel& WithScoreThresholdLastUpdatedAt(Aws::Utils::DateTime&& value) { SetScoreThresholdLastUpdatedAt(std::move(value)); return *this;}


    /**
     * <p>A description of the most recent details about accessing the
     * <code>MLModel</code>.</p>
     */
    inline const Aws::String& GetMessage() const{ return m_message; }

    /**
     * <p>A description of the most recent details about accessing the
     * <code>MLModel</code>.</p>
     */
    inline bool MessageHasBeenSet() const { return m_messageHasBeenSet; }

    /**
     * <p>A description of the most recent details about accessing the
     * <code>MLModel</code>.</p>
     */
    inline void SetMessage(const Aws::String& value) { m_messageHasBeenSet = true; m_message = value; }

    /**
     * <p>A description of the most recent details about accessing the
     * <code>MLModel</code>.</p>
     */
    inline void SetMessage(Aws::String&& value) { m_messageHasBeenSet = true; m_message = std::move(value); }

    /**
     * <p>A description of the most recent details about accessing the
     * <code>MLModel</code>.</p>
     */
    inline void SetMessage(const char* value) { m_messageHasBeenSet = true; m_message.assign(value); }

    /**
     * <p>A description of the most recent details about accessing the
     * <code>MLModel</code>.</p>
     */
    inline MLModel& WithMessage(const Aws::String& value) { SetMessage(value); return *this;}

    /**
     * <p>A description of the most recent details about accessing the
     * <code>MLModel</code>.</p>
     */
    inline MLModel& WithMessage(Aws::String&& value) { SetMessage(std::move(value)); return *this;}

    /**
     * <p>A description of the most recent details about accessing the
     * <code>MLModel</code>.</p>
     */
    inline MLModel& WithMessage(const char* value) { SetMessage(value); return *this;}


    inline long long GetComputeTime() const{ return m_computeTime; }


    inline bool ComputeTimeHasBeenSet() const { return m_computeTimeHasBeenSet; }


    inline void SetComputeTime(long long value) { m_computeTimeHasBeenSet = true; m_computeTime = value; }


    inline MLModel& WithComputeTime(long long value) { SetComputeTime(value); return *this;}


    inline const Aws::Utils::DateTime& GetFinishedAt() const{ return m_finishedAt; }


    inline bool FinishedAtHasBeenSet() const { return m_finishedAtHasBeenSet; }


    inline void SetFinishedAt(const Aws::Utils::DateTime& value) { m_finishedAtHasBeenSet = true; m_finishedAt = value; }


    inline void SetFinishedAt(Aws::Utils::DateTime&& value) { m_finishedAtHasBeenSet = true; m_finishedAt = std::move(value); }


    inline MLModel& WithFinishedAt(const Aws::Utils::DateTime& value) { SetFinishedAt(value); return *this;}


    inline MLModel& WithFinishedAt(Aws::Utils::DateTime&& value) { SetFinishedAt(std::move(value)); return *this;}


    inline const Aws::Utils::DateTime& GetStartedAt() const{ return m_startedAt; }


    inline bool StartedAtHasBeenSet() const { return m_startedAtHasBeenSet; }


    inline void SetStartedAt(const Aws::Utils::DateTime& value) { m_startedAtHasBeenSet = true; m_startedAt = value; }


    inline void SetStartedAt(Aws::Utils::DateTime&& value) { m_startedAtHasBeenSet = true; m_startedAt = std::move(value); }


    inline MLModel& WithStartedAt(const Aws::Utils::DateTime& value) { SetStartedAt(value); return *this;}


    inline MLModel& WithStartedAt(Aws::Utils::DateTime&& value) { SetStartedAt(std::move(value)); return *this;}

  private:

    Aws::String m_mLModelId;
    bool m_mLModelIdHasBeenSet;

    Aws::String m_trainingDataSourceId;
    bool m_trainingDataSourceIdHasBeenSet;

    Aws::String m_createdByIamUser;
    bool m_createdByIamUserHasBeenSet;

    Aws::Utils::DateTime m_createdAt;
    bool m_createdAtHasBeenSet;

    Aws::Utils::DateTime m_lastUpdatedAt;
    bool m_lastUpdatedAtHasBeenSet;

    Aws::String m_name;
    bool m_nameHasBeenSet;

    EntityStatus m_status;
    bool m_statusHasBeenSet;

    long long m_sizeInBytes;
    bool m_sizeInBytesHasBeenSet;

    RealtimeEndpointInfo m_endpointInfo;
    bool m_endpointInfoHasBeenSet;

    Aws::Map<Aws::String, Aws::String> m_trainingParameters;
    bool m_trainingParametersHasBeenSet;

    Aws::String m_inputDataLocationS3;
    bool m_inputDataLocationS3HasBeenSet;

    Algorithm m_algorithm;
    bool m_algorithmHasBeenSet;

    MLModelType m_mLModelType;
    bool m_mLModelTypeHasBeenSet;

    double m_scoreThreshold;
    bool m_scoreThresholdHasBeenSet;

    Aws::Utils::DateTime m_scoreThresholdLastUpdatedAt;
    bool m_scoreThresholdLastUpdatedAtHasBeenSet;

    Aws::String m_message;
    bool m_messageHasBeenSet;

    long long m_computeTime;
    bool m_computeTimeHasBeenSet;

    Aws::Utils::DateTime m_finishedAt;
    bool m_finishedAtHasBeenSet;

    Aws::Utils::DateTime m_startedAt;
    bool m_startedAtHasBeenSet;
  };

} // namespace Model
} // namespace MachineLearning
} // namespace Aws