1 /** 2 * Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. 3 * SPDX-License-Identifier: Apache-2.0. 4 */ 5 6 #pragma once 7 #include <aws/machinelearning/MachineLearning_EXPORTS.h> 8 #include <aws/core/utils/memory/stl/AWSString.h> 9 #include <aws/core/utils/DateTime.h> 10 #include <aws/machinelearning/model/EntityStatus.h> 11 #include <aws/machinelearning/model/RealtimeEndpointInfo.h> 12 #include <aws/core/utils/memory/stl/AWSMap.h> 13 #include <aws/machinelearning/model/MLModelType.h> 14 #include <utility> 15 16 namespace Aws 17 { 18 template<typename RESULT_TYPE> 19 class AmazonWebServiceResult; 20 21 namespace Utils 22 { 23 namespace Json 24 { 25 class JsonValue; 26 } // namespace Json 27 } // namespace Utils 28 namespace MachineLearning 29 { 30 namespace Model 31 { 32 /** 33 * <p>Represents the output of a <code>GetMLModel</code> operation, and provides 34 * detailed information about a <code>MLModel</code>.</p><p><h3>See Also:</h3> <a 35 * href="http://docs.aws.amazon.com/goto/WebAPI/machinelearning-2014-12-12/GetMLModelOutput">AWS 36 * API Reference</a></p> 37 */ 38 class AWS_MACHINELEARNING_API GetMLModelResult 39 { 40 public: 41 GetMLModelResult(); 42 GetMLModelResult(const Aws::AmazonWebServiceResult<Aws::Utils::Json::JsonValue>& result); 43 GetMLModelResult& operator=(const Aws::AmazonWebServiceResult<Aws::Utils::Json::JsonValue>& result); 44 45 46 /** 47 * <p>The MLModel ID, which is same as the <code>MLModelId</code> in the 48 * request.</p> 49 */ GetMLModelId()50 inline const Aws::String& GetMLModelId() const{ return m_mLModelId; } 51 52 /** 53 * <p>The MLModel ID, which is same as the <code>MLModelId</code> in the 54 * request.</p> 55 */ SetMLModelId(const Aws::String & value)56 inline void SetMLModelId(const Aws::String& value) { m_mLModelId = value; } 57 58 /** 59 * <p>The MLModel ID, which is same as the <code>MLModelId</code> in the 60 * request.</p> 61 */ SetMLModelId(Aws::String && value)62 inline void SetMLModelId(Aws::String&& value) { m_mLModelId = std::move(value); } 63 64 /** 65 * <p>The MLModel ID, which is same as the <code>MLModelId</code> in the 66 * request.</p> 67 */ SetMLModelId(const char * value)68 inline void SetMLModelId(const char* value) { m_mLModelId.assign(value); } 69 70 /** 71 * <p>The MLModel ID, which is same as the <code>MLModelId</code> in the 72 * request.</p> 73 */ WithMLModelId(const Aws::String & value)74 inline GetMLModelResult& WithMLModelId(const Aws::String& value) { SetMLModelId(value); return *this;} 75 76 /** 77 * <p>The MLModel ID, which is same as the <code>MLModelId</code> in the 78 * request.</p> 79 */ WithMLModelId(Aws::String && value)80 inline GetMLModelResult& WithMLModelId(Aws::String&& value) { SetMLModelId(std::move(value)); return *this;} 81 82 /** 83 * <p>The MLModel ID, which is same as the <code>MLModelId</code> in the 84 * request.</p> 85 */ WithMLModelId(const char * value)86 inline GetMLModelResult& WithMLModelId(const char* value) { SetMLModelId(value); return *this;} 87 88 89 /** 90 * <p>The ID of the training <code>DataSource</code>.</p> 91 */ GetTrainingDataSourceId()92 inline const Aws::String& GetTrainingDataSourceId() const{ return m_trainingDataSourceId; } 93 94 /** 95 * <p>The ID of the training <code>DataSource</code>.</p> 96 */ SetTrainingDataSourceId(const Aws::String & value)97 inline void SetTrainingDataSourceId(const Aws::String& value) { m_trainingDataSourceId = value; } 98 99 /** 100 * <p>The ID of the training <code>DataSource</code>.</p> 101 */ SetTrainingDataSourceId(Aws::String && value)102 inline void SetTrainingDataSourceId(Aws::String&& value) { m_trainingDataSourceId = std::move(value); } 103 104 /** 105 * <p>The ID of the training <code>DataSource</code>.</p> 106 */ SetTrainingDataSourceId(const char * value)107 inline void SetTrainingDataSourceId(const char* value) { m_trainingDataSourceId.assign(value); } 108 109 /** 110 * <p>The ID of the training <code>DataSource</code>.</p> 111 */ WithTrainingDataSourceId(const Aws::String & value)112 inline GetMLModelResult& WithTrainingDataSourceId(const Aws::String& value) { SetTrainingDataSourceId(value); return *this;} 113 114 /** 115 * <p>The ID of the training <code>DataSource</code>.</p> 116 */ WithTrainingDataSourceId(Aws::String && value)117 inline GetMLModelResult& WithTrainingDataSourceId(Aws::String&& value) { SetTrainingDataSourceId(std::move(value)); return *this;} 118 119 /** 120 * <p>The ID of the training <code>DataSource</code>.</p> 121 */ WithTrainingDataSourceId(const char * value)122 inline GetMLModelResult& WithTrainingDataSourceId(const char* value) { SetTrainingDataSourceId(value); return *this;} 123 124 125 /** 126 * <p>The AWS user account from which the <code>MLModel</code> was created. The 127 * account type can be either an AWS root account or an AWS Identity and Access 128 * Management (IAM) user account.</p> 129 */ GetCreatedByIamUser()130 inline const Aws::String& GetCreatedByIamUser() const{ return m_createdByIamUser; } 131 132 /** 133 * <p>The AWS user account from which the <code>MLModel</code> was created. The 134 * account type can be either an AWS root account or an AWS Identity and Access 135 * Management (IAM) user account.</p> 136 */ SetCreatedByIamUser(const Aws::String & value)137 inline void SetCreatedByIamUser(const Aws::String& value) { m_createdByIamUser = value; } 138 139 /** 140 * <p>The AWS user account from which the <code>MLModel</code> was created. The 141 * account type can be either an AWS root account or an AWS Identity and Access 142 * Management (IAM) user account.</p> 143 */ SetCreatedByIamUser(Aws::String && value)144 inline void SetCreatedByIamUser(Aws::String&& value) { m_createdByIamUser = std::move(value); } 145 146 /** 147 * <p>The AWS user account from which the <code>MLModel</code> was created. The 148 * account type can be either an AWS root account or an AWS Identity and Access 149 * Management (IAM) user account.</p> 150 */ SetCreatedByIamUser(const char * value)151 inline void SetCreatedByIamUser(const char* value) { m_createdByIamUser.assign(value); } 152 153 /** 154 * <p>The AWS user account from which the <code>MLModel</code> was created. The 155 * account type can be either an AWS root account or an AWS Identity and Access 156 * Management (IAM) user account.</p> 157 */ WithCreatedByIamUser(const Aws::String & value)158 inline GetMLModelResult& WithCreatedByIamUser(const Aws::String& value) { SetCreatedByIamUser(value); return *this;} 159 160 /** 161 * <p>The AWS user account from which the <code>MLModel</code> was created. The 162 * account type can be either an AWS root account or an AWS Identity and Access 163 * Management (IAM) user account.</p> 164 */ WithCreatedByIamUser(Aws::String && value)165 inline GetMLModelResult& WithCreatedByIamUser(Aws::String&& value) { SetCreatedByIamUser(std::move(value)); return *this;} 166 167 /** 168 * <p>The AWS user account from which the <code>MLModel</code> was created. The 169 * account type can be either an AWS root account or an AWS Identity and Access 170 * Management (IAM) user account.</p> 171 */ WithCreatedByIamUser(const char * value)172 inline GetMLModelResult& WithCreatedByIamUser(const char* value) { SetCreatedByIamUser(value); return *this;} 173 174 175 /** 176 * <p>The time that the <code>MLModel</code> was created. The time is expressed in 177 * epoch time.</p> 178 */ GetCreatedAt()179 inline const Aws::Utils::DateTime& GetCreatedAt() const{ return m_createdAt; } 180 181 /** 182 * <p>The time that the <code>MLModel</code> was created. The time is expressed in 183 * epoch time.</p> 184 */ SetCreatedAt(const Aws::Utils::DateTime & value)185 inline void SetCreatedAt(const Aws::Utils::DateTime& value) { m_createdAt = value; } 186 187 /** 188 * <p>The time that the <code>MLModel</code> was created. The time is expressed in 189 * epoch time.</p> 190 */ SetCreatedAt(Aws::Utils::DateTime && value)191 inline void SetCreatedAt(Aws::Utils::DateTime&& value) { m_createdAt = std::move(value); } 192 193 /** 194 * <p>The time that the <code>MLModel</code> was created. The time is expressed in 195 * epoch time.</p> 196 */ WithCreatedAt(const Aws::Utils::DateTime & value)197 inline GetMLModelResult& WithCreatedAt(const Aws::Utils::DateTime& value) { SetCreatedAt(value); return *this;} 198 199 /** 200 * <p>The time that the <code>MLModel</code> was created. The time is expressed in 201 * epoch time.</p> 202 */ WithCreatedAt(Aws::Utils::DateTime && value)203 inline GetMLModelResult& WithCreatedAt(Aws::Utils::DateTime&& value) { SetCreatedAt(std::move(value)); return *this;} 204 205 206 /** 207 * <p>The time of the most recent edit to the <code>MLModel</code>. The time is 208 * expressed in epoch time.</p> 209 */ GetLastUpdatedAt()210 inline const Aws::Utils::DateTime& GetLastUpdatedAt() const{ return m_lastUpdatedAt; } 211 212 /** 213 * <p>The time of the most recent edit to the <code>MLModel</code>. The time is 214 * expressed in epoch time.</p> 215 */ SetLastUpdatedAt(const Aws::Utils::DateTime & value)216 inline void SetLastUpdatedAt(const Aws::Utils::DateTime& value) { m_lastUpdatedAt = value; } 217 218 /** 219 * <p>The time of the most recent edit to the <code>MLModel</code>. The time is 220 * expressed in epoch time.</p> 221 */ SetLastUpdatedAt(Aws::Utils::DateTime && value)222 inline void SetLastUpdatedAt(Aws::Utils::DateTime&& value) { m_lastUpdatedAt = std::move(value); } 223 224 /** 225 * <p>The time of the most recent edit to the <code>MLModel</code>. The time is 226 * expressed in epoch time.</p> 227 */ WithLastUpdatedAt(const Aws::Utils::DateTime & value)228 inline GetMLModelResult& WithLastUpdatedAt(const Aws::Utils::DateTime& value) { SetLastUpdatedAt(value); return *this;} 229 230 /** 231 * <p>The time of the most recent edit to the <code>MLModel</code>. The time is 232 * expressed in epoch time.</p> 233 */ WithLastUpdatedAt(Aws::Utils::DateTime && value)234 inline GetMLModelResult& WithLastUpdatedAt(Aws::Utils::DateTime&& value) { SetLastUpdatedAt(std::move(value)); return *this;} 235 236 237 /** 238 * <p>A user-supplied name or description of the <code>MLModel</code>.</p> 239 */ GetName()240 inline const Aws::String& GetName() const{ return m_name; } 241 242 /** 243 * <p>A user-supplied name or description of the <code>MLModel</code>.</p> 244 */ SetName(const Aws::String & value)245 inline void SetName(const Aws::String& value) { m_name = value; } 246 247 /** 248 * <p>A user-supplied name or description of the <code>MLModel</code>.</p> 249 */ SetName(Aws::String && value)250 inline void SetName(Aws::String&& value) { m_name = std::move(value); } 251 252 /** 253 * <p>A user-supplied name or description of the <code>MLModel</code>.</p> 254 */ SetName(const char * value)255 inline void SetName(const char* value) { m_name.assign(value); } 256 257 /** 258 * <p>A user-supplied name or description of the <code>MLModel</code>.</p> 259 */ WithName(const Aws::String & value)260 inline GetMLModelResult& WithName(const Aws::String& value) { SetName(value); return *this;} 261 262 /** 263 * <p>A user-supplied name or description of the <code>MLModel</code>.</p> 264 */ WithName(Aws::String && value)265 inline GetMLModelResult& WithName(Aws::String&& value) { SetName(std::move(value)); return *this;} 266 267 /** 268 * <p>A user-supplied name or description of the <code>MLModel</code>.</p> 269 */ WithName(const char * value)270 inline GetMLModelResult& WithName(const char* value) { SetName(value); return *this;} 271 272 273 /** 274 * <p>The current status of the <code>MLModel</code>. This element can have one of 275 * the following values:</p> <ul> <li> <p> <code>PENDING</code> - Amazon Machine 276 * Learning (Amazon ML) submitted a request to describe a <code>MLModel</code>.</p> 277 * </li> <li> <p> <code>INPROGRESS</code> - The request is processing.</p> </li> 278 * <li> <p> <code>FAILED</code> - The request did not run to completion. The ML 279 * model isn't usable.</p> </li> <li> <p> <code>COMPLETED</code> - The request 280 * completed successfully.</p> </li> <li> <p> <code>DELETED</code> - The 281 * <code>MLModel</code> is marked as deleted. It isn't usable.</p> </li> </ul> 282 */ GetStatus()283 inline const EntityStatus& GetStatus() const{ return m_status; } 284 285 /** 286 * <p>The current status of the <code>MLModel</code>. This element can have one of 287 * the following values:</p> <ul> <li> <p> <code>PENDING</code> - Amazon Machine 288 * Learning (Amazon ML) submitted a request to describe a <code>MLModel</code>.</p> 289 * </li> <li> <p> <code>INPROGRESS</code> - The request is processing.</p> </li> 290 * <li> <p> <code>FAILED</code> - The request did not run to completion. The ML 291 * model isn't usable.</p> </li> <li> <p> <code>COMPLETED</code> - The request 292 * completed successfully.</p> </li> <li> <p> <code>DELETED</code> - The 293 * <code>MLModel</code> is marked as deleted. It isn't usable.</p> </li> </ul> 294 */ SetStatus(const EntityStatus & value)295 inline void SetStatus(const EntityStatus& value) { m_status = value; } 296 297 /** 298 * <p>The current status of the <code>MLModel</code>. This element can have one of 299 * the following values:</p> <ul> <li> <p> <code>PENDING</code> - Amazon Machine 300 * Learning (Amazon ML) submitted a request to describe a <code>MLModel</code>.</p> 301 * </li> <li> <p> <code>INPROGRESS</code> - The request is processing.</p> </li> 302 * <li> <p> <code>FAILED</code> - The request did not run to completion. The ML 303 * model isn't usable.</p> </li> <li> <p> <code>COMPLETED</code> - The request 304 * completed successfully.</p> </li> <li> <p> <code>DELETED</code> - The 305 * <code>MLModel</code> is marked as deleted. It isn't usable.</p> </li> </ul> 306 */ SetStatus(EntityStatus && value)307 inline void SetStatus(EntityStatus&& value) { m_status = std::move(value); } 308 309 /** 310 * <p>The current status of the <code>MLModel</code>. This element can have one of 311 * the following values:</p> <ul> <li> <p> <code>PENDING</code> - Amazon Machine 312 * Learning (Amazon ML) submitted a request to describe a <code>MLModel</code>.</p> 313 * </li> <li> <p> <code>INPROGRESS</code> - The request is processing.</p> </li> 314 * <li> <p> <code>FAILED</code> - The request did not run to completion. The ML 315 * model isn't usable.</p> </li> <li> <p> <code>COMPLETED</code> - The request 316 * completed successfully.</p> </li> <li> <p> <code>DELETED</code> - The 317 * <code>MLModel</code> is marked as deleted. It isn't usable.</p> </li> </ul> 318 */ WithStatus(const EntityStatus & value)319 inline GetMLModelResult& WithStatus(const EntityStatus& value) { SetStatus(value); return *this;} 320 321 /** 322 * <p>The current status of the <code>MLModel</code>. This element can have one of 323 * the following values:</p> <ul> <li> <p> <code>PENDING</code> - Amazon Machine 324 * Learning (Amazon ML) submitted a request to describe a <code>MLModel</code>.</p> 325 * </li> <li> <p> <code>INPROGRESS</code> - The request is processing.</p> </li> 326 * <li> <p> <code>FAILED</code> - The request did not run to completion. The ML 327 * model isn't usable.</p> </li> <li> <p> <code>COMPLETED</code> - The request 328 * completed successfully.</p> </li> <li> <p> <code>DELETED</code> - The 329 * <code>MLModel</code> is marked as deleted. It isn't usable.</p> </li> </ul> 330 */ WithStatus(EntityStatus && value)331 inline GetMLModelResult& WithStatus(EntityStatus&& value) { SetStatus(std::move(value)); return *this;} 332 333 334 GetSizeInBytes()335 inline long long GetSizeInBytes() const{ return m_sizeInBytes; } 336 337 SetSizeInBytes(long long value)338 inline void SetSizeInBytes(long long value) { m_sizeInBytes = value; } 339 340 WithSizeInBytes(long long value)341 inline GetMLModelResult& WithSizeInBytes(long long value) { SetSizeInBytes(value); return *this;} 342 343 344 /** 345 * <p>The current endpoint of the <code>MLModel</code> </p> 346 */ GetEndpointInfo()347 inline const RealtimeEndpointInfo& GetEndpointInfo() const{ return m_endpointInfo; } 348 349 /** 350 * <p>The current endpoint of the <code>MLModel</code> </p> 351 */ SetEndpointInfo(const RealtimeEndpointInfo & value)352 inline void SetEndpointInfo(const RealtimeEndpointInfo& value) { m_endpointInfo = value; } 353 354 /** 355 * <p>The current endpoint of the <code>MLModel</code> </p> 356 */ SetEndpointInfo(RealtimeEndpointInfo && value)357 inline void SetEndpointInfo(RealtimeEndpointInfo&& value) { m_endpointInfo = std::move(value); } 358 359 /** 360 * <p>The current endpoint of the <code>MLModel</code> </p> 361 */ WithEndpointInfo(const RealtimeEndpointInfo & value)362 inline GetMLModelResult& WithEndpointInfo(const RealtimeEndpointInfo& value) { SetEndpointInfo(value); return *this;} 363 364 /** 365 * <p>The current endpoint of the <code>MLModel</code> </p> 366 */ WithEndpointInfo(RealtimeEndpointInfo && value)367 inline GetMLModelResult& WithEndpointInfo(RealtimeEndpointInfo&& value) { SetEndpointInfo(std::move(value)); return *this;} 368 369 370 /** 371 * <p>A list of the training parameters in the <code>MLModel</code>. The list is 372 * implemented as a map of key-value pairs.</p> <p>The following is the current set 373 * of training parameters:</p> <ul> <li> <p> <code>sgd.maxMLModelSizeInBytes</code> 374 * - The maximum allowed size of the model. Depending on the input data, the size 375 * of the model might affect its performance.</p> <p> The value is an integer that 376 * ranges from <code>100000</code> to <code>2147483648</code>. The default value is 377 * <code>33554432</code>.</p> </li> <li> <p> <code>sgd.maxPasses</code> - The 378 * number of times that the training process traverses the observations to build 379 * the <code>MLModel</code>. The value is an integer that ranges from 380 * <code>1</code> to <code>10000</code>. The default value is <code>10</code>.</p> 381 * </li> <li> <p> <code>sgd.shuffleType</code> - Whether Amazon ML shuffles the 382 * training data. Shuffling data improves a model's ability to find the optimal 383 * solution for a variety of data types. The valid values are <code>auto</code> and 384 * <code>none</code>. The default value is <code>none</code>. We strongly recommend 385 * that you shuffle your data.</p> </li> <li> <p> 386 * <code>sgd.l1RegularizationAmount</code> - The coefficient regularization L1 387 * norm. It controls overfitting the data by penalizing large coefficients. This 388 * tends to drive coefficients to zero, resulting in a sparse feature set. If you 389 * use this parameter, start by specifying a small value, such as 390 * <code>1.0E-08</code>.</p> <p>The value is a double that ranges from 391 * <code>0</code> to <code>MAX_DOUBLE</code>. The default is to not use L1 392 * normalization. This parameter can't be used when <code>L2</code> is specified. 393 * Use this parameter sparingly.</p> </li> <li> <p> 394 * <code>sgd.l2RegularizationAmount</code> - The coefficient regularization L2 395 * norm. It controls overfitting the data by penalizing large coefficients. This 396 * tends to drive coefficients to small, nonzero values. If you use this parameter, 397 * start by specifying a small value, such as <code>1.0E-08</code>.</p> <p>The 398 * value is a double that ranges from <code>0</code> to <code>MAX_DOUBLE</code>. 399 * The default is to not use L2 normalization. This parameter can't be used when 400 * <code>L1</code> is specified. Use this parameter sparingly.</p> </li> </ul> 401 */ GetTrainingParameters()402 inline const Aws::Map<Aws::String, Aws::String>& GetTrainingParameters() const{ return m_trainingParameters; } 403 404 /** 405 * <p>A list of the training parameters in the <code>MLModel</code>. The list is 406 * implemented as a map of key-value pairs.</p> <p>The following is the current set 407 * of training parameters:</p> <ul> <li> <p> <code>sgd.maxMLModelSizeInBytes</code> 408 * - The maximum allowed size of the model. Depending on the input data, the size 409 * of the model might affect its performance.</p> <p> The value is an integer that 410 * ranges from <code>100000</code> to <code>2147483648</code>. The default value is 411 * <code>33554432</code>.</p> </li> <li> <p> <code>sgd.maxPasses</code> - The 412 * number of times that the training process traverses the observations to build 413 * the <code>MLModel</code>. The value is an integer that ranges from 414 * <code>1</code> to <code>10000</code>. The default value is <code>10</code>.</p> 415 * </li> <li> <p> <code>sgd.shuffleType</code> - Whether Amazon ML shuffles the 416 * training data. Shuffling data improves a model's ability to find the optimal 417 * solution for a variety of data types. The valid values are <code>auto</code> and 418 * <code>none</code>. The default value is <code>none</code>. We strongly recommend 419 * that you shuffle your data.</p> </li> <li> <p> 420 * <code>sgd.l1RegularizationAmount</code> - The coefficient regularization L1 421 * norm. It controls overfitting the data by penalizing large coefficients. This 422 * tends to drive coefficients to zero, resulting in a sparse feature set. If you 423 * use this parameter, start by specifying a small value, such as 424 * <code>1.0E-08</code>.</p> <p>The value is a double that ranges from 425 * <code>0</code> to <code>MAX_DOUBLE</code>. The default is to not use L1 426 * normalization. This parameter can't be used when <code>L2</code> is specified. 427 * Use this parameter sparingly.</p> </li> <li> <p> 428 * <code>sgd.l2RegularizationAmount</code> - The coefficient regularization L2 429 * norm. It controls overfitting the data by penalizing large coefficients. This 430 * tends to drive coefficients to small, nonzero values. If you use this parameter, 431 * start by specifying a small value, such as <code>1.0E-08</code>.</p> <p>The 432 * value is a double that ranges from <code>0</code> to <code>MAX_DOUBLE</code>. 433 * The default is to not use L2 normalization. This parameter can't be used when 434 * <code>L1</code> is specified. Use this parameter sparingly.</p> </li> </ul> 435 */ SetTrainingParameters(const Aws::Map<Aws::String,Aws::String> & value)436 inline void SetTrainingParameters(const Aws::Map<Aws::String, Aws::String>& value) { m_trainingParameters = value; } 437 438 /** 439 * <p>A list of the training parameters in the <code>MLModel</code>. The list is 440 * implemented as a map of key-value pairs.</p> <p>The following is the current set 441 * of training parameters:</p> <ul> <li> <p> <code>sgd.maxMLModelSizeInBytes</code> 442 * - The maximum allowed size of the model. Depending on the input data, the size 443 * of the model might affect its performance.</p> <p> The value is an integer that 444 * ranges from <code>100000</code> to <code>2147483648</code>. The default value is 445 * <code>33554432</code>.</p> </li> <li> <p> <code>sgd.maxPasses</code> - The 446 * number of times that the training process traverses the observations to build 447 * the <code>MLModel</code>. The value is an integer that ranges from 448 * <code>1</code> to <code>10000</code>. The default value is <code>10</code>.</p> 449 * </li> <li> <p> <code>sgd.shuffleType</code> - Whether Amazon ML shuffles the 450 * training data. Shuffling data improves a model's ability to find the optimal 451 * solution for a variety of data types. The valid values are <code>auto</code> and 452 * <code>none</code>. The default value is <code>none</code>. We strongly recommend 453 * that you shuffle your data.</p> </li> <li> <p> 454 * <code>sgd.l1RegularizationAmount</code> - The coefficient regularization L1 455 * norm. It controls overfitting the data by penalizing large coefficients. This 456 * tends to drive coefficients to zero, resulting in a sparse feature set. If you 457 * use this parameter, start by specifying a small value, such as 458 * <code>1.0E-08</code>.</p> <p>The value is a double that ranges from 459 * <code>0</code> to <code>MAX_DOUBLE</code>. The default is to not use L1 460 * normalization. This parameter can't be used when <code>L2</code> is specified. 461 * Use this parameter sparingly.</p> </li> <li> <p> 462 * <code>sgd.l2RegularizationAmount</code> - The coefficient regularization L2 463 * norm. It controls overfitting the data by penalizing large coefficients. This 464 * tends to drive coefficients to small, nonzero values. If you use this parameter, 465 * start by specifying a small value, such as <code>1.0E-08</code>.</p> <p>The 466 * value is a double that ranges from <code>0</code> to <code>MAX_DOUBLE</code>. 467 * The default is to not use L2 normalization. This parameter can't be used when 468 * <code>L1</code> is specified. Use this parameter sparingly.</p> </li> </ul> 469 */ SetTrainingParameters(Aws::Map<Aws::String,Aws::String> && value)470 inline void SetTrainingParameters(Aws::Map<Aws::String, Aws::String>&& value) { m_trainingParameters = std::move(value); } 471 472 /** 473 * <p>A list of the training parameters in the <code>MLModel</code>. The list is 474 * implemented as a map of key-value pairs.</p> <p>The following is the current set 475 * of training parameters:</p> <ul> <li> <p> <code>sgd.maxMLModelSizeInBytes</code> 476 * - The maximum allowed size of the model. Depending on the input data, the size 477 * of the model might affect its performance.</p> <p> The value is an integer that 478 * ranges from <code>100000</code> to <code>2147483648</code>. The default value is 479 * <code>33554432</code>.</p> </li> <li> <p> <code>sgd.maxPasses</code> - The 480 * number of times that the training process traverses the observations to build 481 * the <code>MLModel</code>. The value is an integer that ranges from 482 * <code>1</code> to <code>10000</code>. The default value is <code>10</code>.</p> 483 * </li> <li> <p> <code>sgd.shuffleType</code> - Whether Amazon ML shuffles the 484 * training data. Shuffling data improves a model's ability to find the optimal 485 * solution for a variety of data types. The valid values are <code>auto</code> and 486 * <code>none</code>. The default value is <code>none</code>. We strongly recommend 487 * that you shuffle your data.</p> </li> <li> <p> 488 * <code>sgd.l1RegularizationAmount</code> - The coefficient regularization L1 489 * norm. It controls overfitting the data by penalizing large coefficients. This 490 * tends to drive coefficients to zero, resulting in a sparse feature set. If you 491 * use this parameter, start by specifying a small value, such as 492 * <code>1.0E-08</code>.</p> <p>The value is a double that ranges from 493 * <code>0</code> to <code>MAX_DOUBLE</code>. The default is to not use L1 494 * normalization. This parameter can't be used when <code>L2</code> is specified. 495 * Use this parameter sparingly.</p> </li> <li> <p> 496 * <code>sgd.l2RegularizationAmount</code> - The coefficient regularization L2 497 * norm. It controls overfitting the data by penalizing large coefficients. This 498 * tends to drive coefficients to small, nonzero values. If you use this parameter, 499 * start by specifying a small value, such as <code>1.0E-08</code>.</p> <p>The 500 * value is a double that ranges from <code>0</code> to <code>MAX_DOUBLE</code>. 501 * The default is to not use L2 normalization. This parameter can't be used when 502 * <code>L1</code> is specified. Use this parameter sparingly.</p> </li> </ul> 503 */ WithTrainingParameters(const Aws::Map<Aws::String,Aws::String> & value)504 inline GetMLModelResult& WithTrainingParameters(const Aws::Map<Aws::String, Aws::String>& value) { SetTrainingParameters(value); return *this;} 505 506 /** 507 * <p>A list of the training parameters in the <code>MLModel</code>. The list is 508 * implemented as a map of key-value pairs.</p> <p>The following is the current set 509 * of training parameters:</p> <ul> <li> <p> <code>sgd.maxMLModelSizeInBytes</code> 510 * - The maximum allowed size of the model. Depending on the input data, the size 511 * of the model might affect its performance.</p> <p> The value is an integer that 512 * ranges from <code>100000</code> to <code>2147483648</code>. The default value is 513 * <code>33554432</code>.</p> </li> <li> <p> <code>sgd.maxPasses</code> - The 514 * number of times that the training process traverses the observations to build 515 * the <code>MLModel</code>. The value is an integer that ranges from 516 * <code>1</code> to <code>10000</code>. The default value is <code>10</code>.</p> 517 * </li> <li> <p> <code>sgd.shuffleType</code> - Whether Amazon ML shuffles the 518 * training data. Shuffling data improves a model's ability to find the optimal 519 * solution for a variety of data types. The valid values are <code>auto</code> and 520 * <code>none</code>. The default value is <code>none</code>. We strongly recommend 521 * that you shuffle your data.</p> </li> <li> <p> 522 * <code>sgd.l1RegularizationAmount</code> - The coefficient regularization L1 523 * norm. It controls overfitting the data by penalizing large coefficients. This 524 * tends to drive coefficients to zero, resulting in a sparse feature set. If you 525 * use this parameter, start by specifying a small value, such as 526 * <code>1.0E-08</code>.</p> <p>The value is a double that ranges from 527 * <code>0</code> to <code>MAX_DOUBLE</code>. The default is to not use L1 528 * normalization. This parameter can't be used when <code>L2</code> is specified. 529 * Use this parameter sparingly.</p> </li> <li> <p> 530 * <code>sgd.l2RegularizationAmount</code> - The coefficient regularization L2 531 * norm. It controls overfitting the data by penalizing large coefficients. This 532 * tends to drive coefficients to small, nonzero values. If you use this parameter, 533 * start by specifying a small value, such as <code>1.0E-08</code>.</p> <p>The 534 * value is a double that ranges from <code>0</code> to <code>MAX_DOUBLE</code>. 535 * The default is to not use L2 normalization. This parameter can't be used when 536 * <code>L1</code> is specified. Use this parameter sparingly.</p> </li> </ul> 537 */ WithTrainingParameters(Aws::Map<Aws::String,Aws::String> && value)538 inline GetMLModelResult& WithTrainingParameters(Aws::Map<Aws::String, Aws::String>&& value) { SetTrainingParameters(std::move(value)); return *this;} 539 540 /** 541 * <p>A list of the training parameters in the <code>MLModel</code>. The list is 542 * implemented as a map of key-value pairs.</p> <p>The following is the current set 543 * of training parameters:</p> <ul> <li> <p> <code>sgd.maxMLModelSizeInBytes</code> 544 * - The maximum allowed size of the model. Depending on the input data, the size 545 * of the model might affect its performance.</p> <p> The value is an integer that 546 * ranges from <code>100000</code> to <code>2147483648</code>. The default value is 547 * <code>33554432</code>.</p> </li> <li> <p> <code>sgd.maxPasses</code> - The 548 * number of times that the training process traverses the observations to build 549 * the <code>MLModel</code>. The value is an integer that ranges from 550 * <code>1</code> to <code>10000</code>. The default value is <code>10</code>.</p> 551 * </li> <li> <p> <code>sgd.shuffleType</code> - Whether Amazon ML shuffles the 552 * training data. Shuffling data improves a model's ability to find the optimal 553 * solution for a variety of data types. The valid values are <code>auto</code> and 554 * <code>none</code>. The default value is <code>none</code>. We strongly recommend 555 * that you shuffle your data.</p> </li> <li> <p> 556 * <code>sgd.l1RegularizationAmount</code> - The coefficient regularization L1 557 * norm. It controls overfitting the data by penalizing large coefficients. This 558 * tends to drive coefficients to zero, resulting in a sparse feature set. If you 559 * use this parameter, start by specifying a small value, such as 560 * <code>1.0E-08</code>.</p> <p>The value is a double that ranges from 561 * <code>0</code> to <code>MAX_DOUBLE</code>. The default is to not use L1 562 * normalization. This parameter can't be used when <code>L2</code> is specified. 563 * Use this parameter sparingly.</p> </li> <li> <p> 564 * <code>sgd.l2RegularizationAmount</code> - The coefficient regularization L2 565 * norm. It controls overfitting the data by penalizing large coefficients. This 566 * tends to drive coefficients to small, nonzero values. If you use this parameter, 567 * start by specifying a small value, such as <code>1.0E-08</code>.</p> <p>The 568 * value is a double that ranges from <code>0</code> to <code>MAX_DOUBLE</code>. 569 * The default is to not use L2 normalization. This parameter can't be used when 570 * <code>L1</code> is specified. Use this parameter sparingly.</p> </li> </ul> 571 */ AddTrainingParameters(const Aws::String & key,const Aws::String & value)572 inline GetMLModelResult& AddTrainingParameters(const Aws::String& key, const Aws::String& value) { m_trainingParameters.emplace(key, value); return *this; } 573 574 /** 575 * <p>A list of the training parameters in the <code>MLModel</code>. The list is 576 * implemented as a map of key-value pairs.</p> <p>The following is the current set 577 * of training parameters:</p> <ul> <li> <p> <code>sgd.maxMLModelSizeInBytes</code> 578 * - The maximum allowed size of the model. Depending on the input data, the size 579 * of the model might affect its performance.</p> <p> The value is an integer that 580 * ranges from <code>100000</code> to <code>2147483648</code>. The default value is 581 * <code>33554432</code>.</p> </li> <li> <p> <code>sgd.maxPasses</code> - The 582 * number of times that the training process traverses the observations to build 583 * the <code>MLModel</code>. The value is an integer that ranges from 584 * <code>1</code> to <code>10000</code>. The default value is <code>10</code>.</p> 585 * </li> <li> <p> <code>sgd.shuffleType</code> - Whether Amazon ML shuffles the 586 * training data. Shuffling data improves a model's ability to find the optimal 587 * solution for a variety of data types. The valid values are <code>auto</code> and 588 * <code>none</code>. The default value is <code>none</code>. We strongly recommend 589 * that you shuffle your data.</p> </li> <li> <p> 590 * <code>sgd.l1RegularizationAmount</code> - The coefficient regularization L1 591 * norm. It controls overfitting the data by penalizing large coefficients. This 592 * tends to drive coefficients to zero, resulting in a sparse feature set. If you 593 * use this parameter, start by specifying a small value, such as 594 * <code>1.0E-08</code>.</p> <p>The value is a double that ranges from 595 * <code>0</code> to <code>MAX_DOUBLE</code>. The default is to not use L1 596 * normalization. This parameter can't be used when <code>L2</code> is specified. 597 * Use this parameter sparingly.</p> </li> <li> <p> 598 * <code>sgd.l2RegularizationAmount</code> - The coefficient regularization L2 599 * norm. It controls overfitting the data by penalizing large coefficients. This 600 * tends to drive coefficients to small, nonzero values. If you use this parameter, 601 * start by specifying a small value, such as <code>1.0E-08</code>.</p> <p>The 602 * value is a double that ranges from <code>0</code> to <code>MAX_DOUBLE</code>. 603 * The default is to not use L2 normalization. This parameter can't be used when 604 * <code>L1</code> is specified. Use this parameter sparingly.</p> </li> </ul> 605 */ AddTrainingParameters(Aws::String && key,const Aws::String & value)606 inline GetMLModelResult& AddTrainingParameters(Aws::String&& key, const Aws::String& value) { m_trainingParameters.emplace(std::move(key), value); return *this; } 607 608 /** 609 * <p>A list of the training parameters in the <code>MLModel</code>. The list is 610 * implemented as a map of key-value pairs.</p> <p>The following is the current set 611 * of training parameters:</p> <ul> <li> <p> <code>sgd.maxMLModelSizeInBytes</code> 612 * - The maximum allowed size of the model. Depending on the input data, the size 613 * of the model might affect its performance.</p> <p> The value is an integer that 614 * ranges from <code>100000</code> to <code>2147483648</code>. The default value is 615 * <code>33554432</code>.</p> </li> <li> <p> <code>sgd.maxPasses</code> - The 616 * number of times that the training process traverses the observations to build 617 * the <code>MLModel</code>. The value is an integer that ranges from 618 * <code>1</code> to <code>10000</code>. The default value is <code>10</code>.</p> 619 * </li> <li> <p> <code>sgd.shuffleType</code> - Whether Amazon ML shuffles the 620 * training data. Shuffling data improves a model's ability to find the optimal 621 * solution for a variety of data types. The valid values are <code>auto</code> and 622 * <code>none</code>. The default value is <code>none</code>. We strongly recommend 623 * that you shuffle your data.</p> </li> <li> <p> 624 * <code>sgd.l1RegularizationAmount</code> - The coefficient regularization L1 625 * norm. It controls overfitting the data by penalizing large coefficients. This 626 * tends to drive coefficients to zero, resulting in a sparse feature set. If you 627 * use this parameter, start by specifying a small value, such as 628 * <code>1.0E-08</code>.</p> <p>The value is a double that ranges from 629 * <code>0</code> to <code>MAX_DOUBLE</code>. The default is to not use L1 630 * normalization. This parameter can't be used when <code>L2</code> is specified. 631 * Use this parameter sparingly.</p> </li> <li> <p> 632 * <code>sgd.l2RegularizationAmount</code> - The coefficient regularization L2 633 * norm. It controls overfitting the data by penalizing large coefficients. This 634 * tends to drive coefficients to small, nonzero values. If you use this parameter, 635 * start by specifying a small value, such as <code>1.0E-08</code>.</p> <p>The 636 * value is a double that ranges from <code>0</code> to <code>MAX_DOUBLE</code>. 637 * The default is to not use L2 normalization. This parameter can't be used when 638 * <code>L1</code> is specified. Use this parameter sparingly.</p> </li> </ul> 639 */ AddTrainingParameters(const Aws::String & key,Aws::String && value)640 inline GetMLModelResult& AddTrainingParameters(const Aws::String& key, Aws::String&& value) { m_trainingParameters.emplace(key, std::move(value)); return *this; } 641 642 /** 643 * <p>A list of the training parameters in the <code>MLModel</code>. The list is 644 * implemented as a map of key-value pairs.</p> <p>The following is the current set 645 * of training parameters:</p> <ul> <li> <p> <code>sgd.maxMLModelSizeInBytes</code> 646 * - The maximum allowed size of the model. Depending on the input data, the size 647 * of the model might affect its performance.</p> <p> The value is an integer that 648 * ranges from <code>100000</code> to <code>2147483648</code>. The default value is 649 * <code>33554432</code>.</p> </li> <li> <p> <code>sgd.maxPasses</code> - The 650 * number of times that the training process traverses the observations to build 651 * the <code>MLModel</code>. The value is an integer that ranges from 652 * <code>1</code> to <code>10000</code>. The default value is <code>10</code>.</p> 653 * </li> <li> <p> <code>sgd.shuffleType</code> - Whether Amazon ML shuffles the 654 * training data. Shuffling data improves a model's ability to find the optimal 655 * solution for a variety of data types. The valid values are <code>auto</code> and 656 * <code>none</code>. The default value is <code>none</code>. We strongly recommend 657 * that you shuffle your data.</p> </li> <li> <p> 658 * <code>sgd.l1RegularizationAmount</code> - The coefficient regularization L1 659 * norm. It controls overfitting the data by penalizing large coefficients. This 660 * tends to drive coefficients to zero, resulting in a sparse feature set. If you 661 * use this parameter, start by specifying a small value, such as 662 * <code>1.0E-08</code>.</p> <p>The value is a double that ranges from 663 * <code>0</code> to <code>MAX_DOUBLE</code>. The default is to not use L1 664 * normalization. This parameter can't be used when <code>L2</code> is specified. 665 * Use this parameter sparingly.</p> </li> <li> <p> 666 * <code>sgd.l2RegularizationAmount</code> - The coefficient regularization L2 667 * norm. It controls overfitting the data by penalizing large coefficients. This 668 * tends to drive coefficients to small, nonzero values. If you use this parameter, 669 * start by specifying a small value, such as <code>1.0E-08</code>.</p> <p>The 670 * value is a double that ranges from <code>0</code> to <code>MAX_DOUBLE</code>. 671 * The default is to not use L2 normalization. This parameter can't be used when 672 * <code>L1</code> is specified. Use this parameter sparingly.</p> </li> </ul> 673 */ AddTrainingParameters(Aws::String && key,Aws::String && value)674 inline GetMLModelResult& AddTrainingParameters(Aws::String&& key, Aws::String&& value) { m_trainingParameters.emplace(std::move(key), std::move(value)); return *this; } 675 676 /** 677 * <p>A list of the training parameters in the <code>MLModel</code>. The list is 678 * implemented as a map of key-value pairs.</p> <p>The following is the current set 679 * of training parameters:</p> <ul> <li> <p> <code>sgd.maxMLModelSizeInBytes</code> 680 * - The maximum allowed size of the model. Depending on the input data, the size 681 * of the model might affect its performance.</p> <p> The value is an integer that 682 * ranges from <code>100000</code> to <code>2147483648</code>. The default value is 683 * <code>33554432</code>.</p> </li> <li> <p> <code>sgd.maxPasses</code> - The 684 * number of times that the training process traverses the observations to build 685 * the <code>MLModel</code>. The value is an integer that ranges from 686 * <code>1</code> to <code>10000</code>. The default value is <code>10</code>.</p> 687 * </li> <li> <p> <code>sgd.shuffleType</code> - Whether Amazon ML shuffles the 688 * training data. Shuffling data improves a model's ability to find the optimal 689 * solution for a variety of data types. The valid values are <code>auto</code> and 690 * <code>none</code>. The default value is <code>none</code>. We strongly recommend 691 * that you shuffle your data.</p> </li> <li> <p> 692 * <code>sgd.l1RegularizationAmount</code> - The coefficient regularization L1 693 * norm. It controls overfitting the data by penalizing large coefficients. This 694 * tends to drive coefficients to zero, resulting in a sparse feature set. If you 695 * use this parameter, start by specifying a small value, such as 696 * <code>1.0E-08</code>.</p> <p>The value is a double that ranges from 697 * <code>0</code> to <code>MAX_DOUBLE</code>. The default is to not use L1 698 * normalization. This parameter can't be used when <code>L2</code> is specified. 699 * Use this parameter sparingly.</p> </li> <li> <p> 700 * <code>sgd.l2RegularizationAmount</code> - The coefficient regularization L2 701 * norm. It controls overfitting the data by penalizing large coefficients. This 702 * tends to drive coefficients to small, nonzero values. If you use this parameter, 703 * start by specifying a small value, such as <code>1.0E-08</code>.</p> <p>The 704 * value is a double that ranges from <code>0</code> to <code>MAX_DOUBLE</code>. 705 * The default is to not use L2 normalization. This parameter can't be used when 706 * <code>L1</code> is specified. Use this parameter sparingly.</p> </li> </ul> 707 */ AddTrainingParameters(const char * key,Aws::String && value)708 inline GetMLModelResult& AddTrainingParameters(const char* key, Aws::String&& value) { m_trainingParameters.emplace(key, std::move(value)); return *this; } 709 710 /** 711 * <p>A list of the training parameters in the <code>MLModel</code>. The list is 712 * implemented as a map of key-value pairs.</p> <p>The following is the current set 713 * of training parameters:</p> <ul> <li> <p> <code>sgd.maxMLModelSizeInBytes</code> 714 * - The maximum allowed size of the model. Depending on the input data, the size 715 * of the model might affect its performance.</p> <p> The value is an integer that 716 * ranges from <code>100000</code> to <code>2147483648</code>. The default value is 717 * <code>33554432</code>.</p> </li> <li> <p> <code>sgd.maxPasses</code> - The 718 * number of times that the training process traverses the observations to build 719 * the <code>MLModel</code>. The value is an integer that ranges from 720 * <code>1</code> to <code>10000</code>. The default value is <code>10</code>.</p> 721 * </li> <li> <p> <code>sgd.shuffleType</code> - Whether Amazon ML shuffles the 722 * training data. Shuffling data improves a model's ability to find the optimal 723 * solution for a variety of data types. The valid values are <code>auto</code> and 724 * <code>none</code>. The default value is <code>none</code>. We strongly recommend 725 * that you shuffle your data.</p> </li> <li> <p> 726 * <code>sgd.l1RegularizationAmount</code> - The coefficient regularization L1 727 * norm. It controls overfitting the data by penalizing large coefficients. This 728 * tends to drive coefficients to zero, resulting in a sparse feature set. If you 729 * use this parameter, start by specifying a small value, such as 730 * <code>1.0E-08</code>.</p> <p>The value is a double that ranges from 731 * <code>0</code> to <code>MAX_DOUBLE</code>. The default is to not use L1 732 * normalization. This parameter can't be used when <code>L2</code> is specified. 733 * Use this parameter sparingly.</p> </li> <li> <p> 734 * <code>sgd.l2RegularizationAmount</code> - The coefficient regularization L2 735 * norm. It controls overfitting the data by penalizing large coefficients. This 736 * tends to drive coefficients to small, nonzero values. If you use this parameter, 737 * start by specifying a small value, such as <code>1.0E-08</code>.</p> <p>The 738 * value is a double that ranges from <code>0</code> to <code>MAX_DOUBLE</code>. 739 * The default is to not use L2 normalization. This parameter can't be used when 740 * <code>L1</code> is specified. Use this parameter sparingly.</p> </li> </ul> 741 */ AddTrainingParameters(Aws::String && key,const char * value)742 inline GetMLModelResult& AddTrainingParameters(Aws::String&& key, const char* value) { m_trainingParameters.emplace(std::move(key), value); return *this; } 743 744 /** 745 * <p>A list of the training parameters in the <code>MLModel</code>. The list is 746 * implemented as a map of key-value pairs.</p> <p>The following is the current set 747 * of training parameters:</p> <ul> <li> <p> <code>sgd.maxMLModelSizeInBytes</code> 748 * - The maximum allowed size of the model. Depending on the input data, the size 749 * of the model might affect its performance.</p> <p> The value is an integer that 750 * ranges from <code>100000</code> to <code>2147483648</code>. The default value is 751 * <code>33554432</code>.</p> </li> <li> <p> <code>sgd.maxPasses</code> - The 752 * number of times that the training process traverses the observations to build 753 * the <code>MLModel</code>. The value is an integer that ranges from 754 * <code>1</code> to <code>10000</code>. The default value is <code>10</code>.</p> 755 * </li> <li> <p> <code>sgd.shuffleType</code> - Whether Amazon ML shuffles the 756 * training data. Shuffling data improves a model's ability to find the optimal 757 * solution for a variety of data types. The valid values are <code>auto</code> and 758 * <code>none</code>. The default value is <code>none</code>. We strongly recommend 759 * that you shuffle your data.</p> </li> <li> <p> 760 * <code>sgd.l1RegularizationAmount</code> - The coefficient regularization L1 761 * norm. It controls overfitting the data by penalizing large coefficients. This 762 * tends to drive coefficients to zero, resulting in a sparse feature set. If you 763 * use this parameter, start by specifying a small value, such as 764 * <code>1.0E-08</code>.</p> <p>The value is a double that ranges from 765 * <code>0</code> to <code>MAX_DOUBLE</code>. The default is to not use L1 766 * normalization. This parameter can't be used when <code>L2</code> is specified. 767 * Use this parameter sparingly.</p> </li> <li> <p> 768 * <code>sgd.l2RegularizationAmount</code> - The coefficient regularization L2 769 * norm. It controls overfitting the data by penalizing large coefficients. This 770 * tends to drive coefficients to small, nonzero values. If you use this parameter, 771 * start by specifying a small value, such as <code>1.0E-08</code>.</p> <p>The 772 * value is a double that ranges from <code>0</code> to <code>MAX_DOUBLE</code>. 773 * The default is to not use L2 normalization. This parameter can't be used when 774 * <code>L1</code> is specified. Use this parameter sparingly.</p> </li> </ul> 775 */ AddTrainingParameters(const char * key,const char * value)776 inline GetMLModelResult& AddTrainingParameters(const char* key, const char* value) { m_trainingParameters.emplace(key, value); return *this; } 777 778 779 /** 780 * <p>The location of the data file or directory in Amazon Simple Storage Service 781 * (Amazon S3).</p> 782 */ GetInputDataLocationS3()783 inline const Aws::String& GetInputDataLocationS3() const{ return m_inputDataLocationS3; } 784 785 /** 786 * <p>The location of the data file or directory in Amazon Simple Storage Service 787 * (Amazon S3).</p> 788 */ SetInputDataLocationS3(const Aws::String & value)789 inline void SetInputDataLocationS3(const Aws::String& value) { m_inputDataLocationS3 = value; } 790 791 /** 792 * <p>The location of the data file or directory in Amazon Simple Storage Service 793 * (Amazon S3).</p> 794 */ SetInputDataLocationS3(Aws::String && value)795 inline void SetInputDataLocationS3(Aws::String&& value) { m_inputDataLocationS3 = std::move(value); } 796 797 /** 798 * <p>The location of the data file or directory in Amazon Simple Storage Service 799 * (Amazon S3).</p> 800 */ SetInputDataLocationS3(const char * value)801 inline void SetInputDataLocationS3(const char* value) { m_inputDataLocationS3.assign(value); } 802 803 /** 804 * <p>The location of the data file or directory in Amazon Simple Storage Service 805 * (Amazon S3).</p> 806 */ WithInputDataLocationS3(const Aws::String & value)807 inline GetMLModelResult& WithInputDataLocationS3(const Aws::String& value) { SetInputDataLocationS3(value); return *this;} 808 809 /** 810 * <p>The location of the data file or directory in Amazon Simple Storage Service 811 * (Amazon S3).</p> 812 */ WithInputDataLocationS3(Aws::String && value)813 inline GetMLModelResult& WithInputDataLocationS3(Aws::String&& value) { SetInputDataLocationS3(std::move(value)); return *this;} 814 815 /** 816 * <p>The location of the data file or directory in Amazon Simple Storage Service 817 * (Amazon S3).</p> 818 */ WithInputDataLocationS3(const char * value)819 inline GetMLModelResult& WithInputDataLocationS3(const char* value) { SetInputDataLocationS3(value); return *this;} 820 821 822 /** 823 * <p>Identifies the <code>MLModel</code> category. The following are the available 824 * types: </p> <ul> <li> <p>REGRESSION -- Produces a numeric result. For example, 825 * "What price should a house be listed at?"</p> </li> <li> <p>BINARY -- Produces 826 * one of two possible results. For example, "Is this an e-commerce website?"</p> 827 * </li> <li> <p>MULTICLASS -- Produces one of several possible results. For 828 * example, "Is this a HIGH, LOW or MEDIUM risk trade?"</p> </li> </ul> 829 */ GetMLModelType()830 inline const MLModelType& GetMLModelType() const{ return m_mLModelType; } 831 832 /** 833 * <p>Identifies the <code>MLModel</code> category. The following are the available 834 * types: </p> <ul> <li> <p>REGRESSION -- Produces a numeric result. For example, 835 * "What price should a house be listed at?"</p> </li> <li> <p>BINARY -- Produces 836 * one of two possible results. For example, "Is this an e-commerce website?"</p> 837 * </li> <li> <p>MULTICLASS -- Produces one of several possible results. For 838 * example, "Is this a HIGH, LOW or MEDIUM risk trade?"</p> </li> </ul> 839 */ SetMLModelType(const MLModelType & value)840 inline void SetMLModelType(const MLModelType& value) { m_mLModelType = value; } 841 842 /** 843 * <p>Identifies the <code>MLModel</code> category. The following are the available 844 * types: </p> <ul> <li> <p>REGRESSION -- Produces a numeric result. For example, 845 * "What price should a house be listed at?"</p> </li> <li> <p>BINARY -- Produces 846 * one of two possible results. For example, "Is this an e-commerce website?"</p> 847 * </li> <li> <p>MULTICLASS -- Produces one of several possible results. For 848 * example, "Is this a HIGH, LOW or MEDIUM risk trade?"</p> </li> </ul> 849 */ SetMLModelType(MLModelType && value)850 inline void SetMLModelType(MLModelType&& value) { m_mLModelType = std::move(value); } 851 852 /** 853 * <p>Identifies the <code>MLModel</code> category. The following are the available 854 * types: </p> <ul> <li> <p>REGRESSION -- Produces a numeric result. For example, 855 * "What price should a house be listed at?"</p> </li> <li> <p>BINARY -- Produces 856 * one of two possible results. For example, "Is this an e-commerce website?"</p> 857 * </li> <li> <p>MULTICLASS -- Produces one of several possible results. For 858 * example, "Is this a HIGH, LOW or MEDIUM risk trade?"</p> </li> </ul> 859 */ WithMLModelType(const MLModelType & value)860 inline GetMLModelResult& WithMLModelType(const MLModelType& value) { SetMLModelType(value); return *this;} 861 862 /** 863 * <p>Identifies the <code>MLModel</code> category. The following are the available 864 * types: </p> <ul> <li> <p>REGRESSION -- Produces a numeric result. For example, 865 * "What price should a house be listed at?"</p> </li> <li> <p>BINARY -- Produces 866 * one of two possible results. For example, "Is this an e-commerce website?"</p> 867 * </li> <li> <p>MULTICLASS -- Produces one of several possible results. For 868 * example, "Is this a HIGH, LOW or MEDIUM risk trade?"</p> </li> </ul> 869 */ WithMLModelType(MLModelType && value)870 inline GetMLModelResult& WithMLModelType(MLModelType&& value) { SetMLModelType(std::move(value)); return *this;} 871 872 873 /** 874 * <p>The scoring threshold is used in binary classification <code>MLModel</code> 875 * models. It marks the boundary between a positive prediction and a negative 876 * prediction.</p> <p>Output values greater than or equal to the threshold receive 877 * a positive result from the MLModel, such as <code>true</code>. Output values 878 * less than the threshold receive a negative response from the MLModel, such as 879 * <code>false</code>.</p> 880 */ GetScoreThreshold()881 inline double GetScoreThreshold() const{ return m_scoreThreshold; } 882 883 /** 884 * <p>The scoring threshold is used in binary classification <code>MLModel</code> 885 * models. It marks the boundary between a positive prediction and a negative 886 * prediction.</p> <p>Output values greater than or equal to the threshold receive 887 * a positive result from the MLModel, such as <code>true</code>. Output values 888 * less than the threshold receive a negative response from the MLModel, such as 889 * <code>false</code>.</p> 890 */ SetScoreThreshold(double value)891 inline void SetScoreThreshold(double value) { m_scoreThreshold = value; } 892 893 /** 894 * <p>The scoring threshold is used in binary classification <code>MLModel</code> 895 * models. It marks the boundary between a positive prediction and a negative 896 * prediction.</p> <p>Output values greater than or equal to the threshold receive 897 * a positive result from the MLModel, such as <code>true</code>. Output values 898 * less than the threshold receive a negative response from the MLModel, such as 899 * <code>false</code>.</p> 900 */ WithScoreThreshold(double value)901 inline GetMLModelResult& WithScoreThreshold(double value) { SetScoreThreshold(value); return *this;} 902 903 904 /** 905 * <p>The time of the most recent edit to the <code>ScoreThreshold</code>. The time 906 * is expressed in epoch time.</p> 907 */ GetScoreThresholdLastUpdatedAt()908 inline const Aws::Utils::DateTime& GetScoreThresholdLastUpdatedAt() const{ return m_scoreThresholdLastUpdatedAt; } 909 910 /** 911 * <p>The time of the most recent edit to the <code>ScoreThreshold</code>. The time 912 * is expressed in epoch time.</p> 913 */ SetScoreThresholdLastUpdatedAt(const Aws::Utils::DateTime & value)914 inline void SetScoreThresholdLastUpdatedAt(const Aws::Utils::DateTime& value) { m_scoreThresholdLastUpdatedAt = value; } 915 916 /** 917 * <p>The time of the most recent edit to the <code>ScoreThreshold</code>. The time 918 * is expressed in epoch time.</p> 919 */ SetScoreThresholdLastUpdatedAt(Aws::Utils::DateTime && value)920 inline void SetScoreThresholdLastUpdatedAt(Aws::Utils::DateTime&& value) { m_scoreThresholdLastUpdatedAt = std::move(value); } 921 922 /** 923 * <p>The time of the most recent edit to the <code>ScoreThreshold</code>. The time 924 * is expressed in epoch time.</p> 925 */ WithScoreThresholdLastUpdatedAt(const Aws::Utils::DateTime & value)926 inline GetMLModelResult& WithScoreThresholdLastUpdatedAt(const Aws::Utils::DateTime& value) { SetScoreThresholdLastUpdatedAt(value); return *this;} 927 928 /** 929 * <p>The time of the most recent edit to the <code>ScoreThreshold</code>. The time 930 * is expressed in epoch time.</p> 931 */ WithScoreThresholdLastUpdatedAt(Aws::Utils::DateTime && value)932 inline GetMLModelResult& WithScoreThresholdLastUpdatedAt(Aws::Utils::DateTime&& value) { SetScoreThresholdLastUpdatedAt(std::move(value)); return *this;} 933 934 935 /** 936 * <p>A link to the file that contains logs of the <code>CreateMLModel</code> 937 * operation.</p> 938 */ GetLogUri()939 inline const Aws::String& GetLogUri() const{ return m_logUri; } 940 941 /** 942 * <p>A link to the file that contains logs of the <code>CreateMLModel</code> 943 * operation.</p> 944 */ SetLogUri(const Aws::String & value)945 inline void SetLogUri(const Aws::String& value) { m_logUri = value; } 946 947 /** 948 * <p>A link to the file that contains logs of the <code>CreateMLModel</code> 949 * operation.</p> 950 */ SetLogUri(Aws::String && value)951 inline void SetLogUri(Aws::String&& value) { m_logUri = std::move(value); } 952 953 /** 954 * <p>A link to the file that contains logs of the <code>CreateMLModel</code> 955 * operation.</p> 956 */ SetLogUri(const char * value)957 inline void SetLogUri(const char* value) { m_logUri.assign(value); } 958 959 /** 960 * <p>A link to the file that contains logs of the <code>CreateMLModel</code> 961 * operation.</p> 962 */ WithLogUri(const Aws::String & value)963 inline GetMLModelResult& WithLogUri(const Aws::String& value) { SetLogUri(value); return *this;} 964 965 /** 966 * <p>A link to the file that contains logs of the <code>CreateMLModel</code> 967 * operation.</p> 968 */ WithLogUri(Aws::String && value)969 inline GetMLModelResult& WithLogUri(Aws::String&& value) { SetLogUri(std::move(value)); return *this;} 970 971 /** 972 * <p>A link to the file that contains logs of the <code>CreateMLModel</code> 973 * operation.</p> 974 */ WithLogUri(const char * value)975 inline GetMLModelResult& WithLogUri(const char* value) { SetLogUri(value); return *this;} 976 977 978 /** 979 * <p>A description of the most recent details about accessing the 980 * <code>MLModel</code>.</p> 981 */ GetMessage()982 inline const Aws::String& GetMessage() const{ return m_message; } 983 984 /** 985 * <p>A description of the most recent details about accessing the 986 * <code>MLModel</code>.</p> 987 */ SetMessage(const Aws::String & value)988 inline void SetMessage(const Aws::String& value) { m_message = value; } 989 990 /** 991 * <p>A description of the most recent details about accessing the 992 * <code>MLModel</code>.</p> 993 */ SetMessage(Aws::String && value)994 inline void SetMessage(Aws::String&& value) { m_message = std::move(value); } 995 996 /** 997 * <p>A description of the most recent details about accessing the 998 * <code>MLModel</code>.</p> 999 */ SetMessage(const char * value)1000 inline void SetMessage(const char* value) { m_message.assign(value); } 1001 1002 /** 1003 * <p>A description of the most recent details about accessing the 1004 * <code>MLModel</code>.</p> 1005 */ WithMessage(const Aws::String & value)1006 inline GetMLModelResult& WithMessage(const Aws::String& value) { SetMessage(value); return *this;} 1007 1008 /** 1009 * <p>A description of the most recent details about accessing the 1010 * <code>MLModel</code>.</p> 1011 */ WithMessage(Aws::String && value)1012 inline GetMLModelResult& WithMessage(Aws::String&& value) { SetMessage(std::move(value)); return *this;} 1013 1014 /** 1015 * <p>A description of the most recent details about accessing the 1016 * <code>MLModel</code>.</p> 1017 */ WithMessage(const char * value)1018 inline GetMLModelResult& WithMessage(const char* value) { SetMessage(value); return *this;} 1019 1020 1021 /** 1022 * <p>The approximate CPU time in milliseconds that Amazon Machine Learning spent 1023 * processing the <code>MLModel</code>, normalized and scaled on computation 1024 * resources. <code>ComputeTime</code> is only available if the 1025 * <code>MLModel</code> is in the <code>COMPLETED</code> state.</p> 1026 */ GetComputeTime()1027 inline long long GetComputeTime() const{ return m_computeTime; } 1028 1029 /** 1030 * <p>The approximate CPU time in milliseconds that Amazon Machine Learning spent 1031 * processing the <code>MLModel</code>, normalized and scaled on computation 1032 * resources. <code>ComputeTime</code> is only available if the 1033 * <code>MLModel</code> is in the <code>COMPLETED</code> state.</p> 1034 */ SetComputeTime(long long value)1035 inline void SetComputeTime(long long value) { m_computeTime = value; } 1036 1037 /** 1038 * <p>The approximate CPU time in milliseconds that Amazon Machine Learning spent 1039 * processing the <code>MLModel</code>, normalized and scaled on computation 1040 * resources. <code>ComputeTime</code> is only available if the 1041 * <code>MLModel</code> is in the <code>COMPLETED</code> state.</p> 1042 */ WithComputeTime(long long value)1043 inline GetMLModelResult& WithComputeTime(long long value) { SetComputeTime(value); return *this;} 1044 1045 1046 /** 1047 * <p>The epoch time when Amazon Machine Learning marked the <code>MLModel</code> 1048 * as <code>COMPLETED</code> or <code>FAILED</code>. <code>FinishedAt</code> is 1049 * only available when the <code>MLModel</code> is in the <code>COMPLETED</code> or 1050 * <code>FAILED</code> state.</p> 1051 */ GetFinishedAt()1052 inline const Aws::Utils::DateTime& GetFinishedAt() const{ return m_finishedAt; } 1053 1054 /** 1055 * <p>The epoch time when Amazon Machine Learning marked the <code>MLModel</code> 1056 * as <code>COMPLETED</code> or <code>FAILED</code>. <code>FinishedAt</code> is 1057 * only available when the <code>MLModel</code> is in the <code>COMPLETED</code> or 1058 * <code>FAILED</code> state.</p> 1059 */ SetFinishedAt(const Aws::Utils::DateTime & value)1060 inline void SetFinishedAt(const Aws::Utils::DateTime& value) { m_finishedAt = value; } 1061 1062 /** 1063 * <p>The epoch time when Amazon Machine Learning marked the <code>MLModel</code> 1064 * as <code>COMPLETED</code> or <code>FAILED</code>. <code>FinishedAt</code> is 1065 * only available when the <code>MLModel</code> is in the <code>COMPLETED</code> or 1066 * <code>FAILED</code> state.</p> 1067 */ SetFinishedAt(Aws::Utils::DateTime && value)1068 inline void SetFinishedAt(Aws::Utils::DateTime&& value) { m_finishedAt = std::move(value); } 1069 1070 /** 1071 * <p>The epoch time when Amazon Machine Learning marked the <code>MLModel</code> 1072 * as <code>COMPLETED</code> or <code>FAILED</code>. <code>FinishedAt</code> is 1073 * only available when the <code>MLModel</code> is in the <code>COMPLETED</code> or 1074 * <code>FAILED</code> state.</p> 1075 */ WithFinishedAt(const Aws::Utils::DateTime & value)1076 inline GetMLModelResult& WithFinishedAt(const Aws::Utils::DateTime& value) { SetFinishedAt(value); return *this;} 1077 1078 /** 1079 * <p>The epoch time when Amazon Machine Learning marked the <code>MLModel</code> 1080 * as <code>COMPLETED</code> or <code>FAILED</code>. <code>FinishedAt</code> is 1081 * only available when the <code>MLModel</code> is in the <code>COMPLETED</code> or 1082 * <code>FAILED</code> state.</p> 1083 */ WithFinishedAt(Aws::Utils::DateTime && value)1084 inline GetMLModelResult& WithFinishedAt(Aws::Utils::DateTime&& value) { SetFinishedAt(std::move(value)); return *this;} 1085 1086 1087 /** 1088 * <p>The epoch time when Amazon Machine Learning marked the <code>MLModel</code> 1089 * as <code>INPROGRESS</code>. <code>StartedAt</code> isn't available if the 1090 * <code>MLModel</code> is in the <code>PENDING</code> state.</p> 1091 */ GetStartedAt()1092 inline const Aws::Utils::DateTime& GetStartedAt() const{ return m_startedAt; } 1093 1094 /** 1095 * <p>The epoch time when Amazon Machine Learning marked the <code>MLModel</code> 1096 * as <code>INPROGRESS</code>. <code>StartedAt</code> isn't available if the 1097 * <code>MLModel</code> is in the <code>PENDING</code> state.</p> 1098 */ SetStartedAt(const Aws::Utils::DateTime & value)1099 inline void SetStartedAt(const Aws::Utils::DateTime& value) { m_startedAt = value; } 1100 1101 /** 1102 * <p>The epoch time when Amazon Machine Learning marked the <code>MLModel</code> 1103 * as <code>INPROGRESS</code>. <code>StartedAt</code> isn't available if the 1104 * <code>MLModel</code> is in the <code>PENDING</code> state.</p> 1105 */ SetStartedAt(Aws::Utils::DateTime && value)1106 inline void SetStartedAt(Aws::Utils::DateTime&& value) { m_startedAt = std::move(value); } 1107 1108 /** 1109 * <p>The epoch time when Amazon Machine Learning marked the <code>MLModel</code> 1110 * as <code>INPROGRESS</code>. <code>StartedAt</code> isn't available if the 1111 * <code>MLModel</code> is in the <code>PENDING</code> state.</p> 1112 */ WithStartedAt(const Aws::Utils::DateTime & value)1113 inline GetMLModelResult& WithStartedAt(const Aws::Utils::DateTime& value) { SetStartedAt(value); return *this;} 1114 1115 /** 1116 * <p>The epoch time when Amazon Machine Learning marked the <code>MLModel</code> 1117 * as <code>INPROGRESS</code>. <code>StartedAt</code> isn't available if the 1118 * <code>MLModel</code> is in the <code>PENDING</code> state.</p> 1119 */ WithStartedAt(Aws::Utils::DateTime && value)1120 inline GetMLModelResult& WithStartedAt(Aws::Utils::DateTime&& value) { SetStartedAt(std::move(value)); return *this;} 1121 1122 1123 /** 1124 * <p>The recipe to use when training the <code>MLModel</code>. The 1125 * <code>Recipe</code> provides detailed information about the observation data to 1126 * use during training, and manipulations to perform on the observation data during 1127 * training.</p> <p> <b>Note:</b> This parameter is provided as part of the verbose 1128 * format.</p> 1129 */ GetRecipe()1130 inline const Aws::String& GetRecipe() const{ return m_recipe; } 1131 1132 /** 1133 * <p>The recipe to use when training the <code>MLModel</code>. The 1134 * <code>Recipe</code> provides detailed information about the observation data to 1135 * use during training, and manipulations to perform on the observation data during 1136 * training.</p> <p> <b>Note:</b> This parameter is provided as part of the verbose 1137 * format.</p> 1138 */ SetRecipe(const Aws::String & value)1139 inline void SetRecipe(const Aws::String& value) { m_recipe = value; } 1140 1141 /** 1142 * <p>The recipe to use when training the <code>MLModel</code>. The 1143 * <code>Recipe</code> provides detailed information about the observation data to 1144 * use during training, and manipulations to perform on the observation data during 1145 * training.</p> <p> <b>Note:</b> This parameter is provided as part of the verbose 1146 * format.</p> 1147 */ SetRecipe(Aws::String && value)1148 inline void SetRecipe(Aws::String&& value) { m_recipe = std::move(value); } 1149 1150 /** 1151 * <p>The recipe to use when training the <code>MLModel</code>. The 1152 * <code>Recipe</code> provides detailed information about the observation data to 1153 * use during training, and manipulations to perform on the observation data during 1154 * training.</p> <p> <b>Note:</b> This parameter is provided as part of the verbose 1155 * format.</p> 1156 */ SetRecipe(const char * value)1157 inline void SetRecipe(const char* value) { m_recipe.assign(value); } 1158 1159 /** 1160 * <p>The recipe to use when training the <code>MLModel</code>. The 1161 * <code>Recipe</code> provides detailed information about the observation data to 1162 * use during training, and manipulations to perform on the observation data during 1163 * training.</p> <p> <b>Note:</b> This parameter is provided as part of the verbose 1164 * format.</p> 1165 */ WithRecipe(const Aws::String & value)1166 inline GetMLModelResult& WithRecipe(const Aws::String& value) { SetRecipe(value); return *this;} 1167 1168 /** 1169 * <p>The recipe to use when training the <code>MLModel</code>. The 1170 * <code>Recipe</code> provides detailed information about the observation data to 1171 * use during training, and manipulations to perform on the observation data during 1172 * training.</p> <p> <b>Note:</b> This parameter is provided as part of the verbose 1173 * format.</p> 1174 */ WithRecipe(Aws::String && value)1175 inline GetMLModelResult& WithRecipe(Aws::String&& value) { SetRecipe(std::move(value)); return *this;} 1176 1177 /** 1178 * <p>The recipe to use when training the <code>MLModel</code>. The 1179 * <code>Recipe</code> provides detailed information about the observation data to 1180 * use during training, and manipulations to perform on the observation data during 1181 * training.</p> <p> <b>Note:</b> This parameter is provided as part of the verbose 1182 * format.</p> 1183 */ WithRecipe(const char * value)1184 inline GetMLModelResult& WithRecipe(const char* value) { SetRecipe(value); return *this;} 1185 1186 1187 /** 1188 * <p>The schema used by all of the data files referenced by the 1189 * <code>DataSource</code>.</p> <p> <b>Note:</b> This parameter is provided as part 1190 * of the verbose format.</p> 1191 */ GetSchema()1192 inline const Aws::String& GetSchema() const{ return m_schema; } 1193 1194 /** 1195 * <p>The schema used by all of the data files referenced by the 1196 * <code>DataSource</code>.</p> <p> <b>Note:</b> This parameter is provided as part 1197 * of the verbose format.</p> 1198 */ SetSchema(const Aws::String & value)1199 inline void SetSchema(const Aws::String& value) { m_schema = value; } 1200 1201 /** 1202 * <p>The schema used by all of the data files referenced by the 1203 * <code>DataSource</code>.</p> <p> <b>Note:</b> This parameter is provided as part 1204 * of the verbose format.</p> 1205 */ SetSchema(Aws::String && value)1206 inline void SetSchema(Aws::String&& value) { m_schema = std::move(value); } 1207 1208 /** 1209 * <p>The schema used by all of the data files referenced by the 1210 * <code>DataSource</code>.</p> <p> <b>Note:</b> This parameter is provided as part 1211 * of the verbose format.</p> 1212 */ SetSchema(const char * value)1213 inline void SetSchema(const char* value) { m_schema.assign(value); } 1214 1215 /** 1216 * <p>The schema used by all of the data files referenced by the 1217 * <code>DataSource</code>.</p> <p> <b>Note:</b> This parameter is provided as part 1218 * of the verbose format.</p> 1219 */ WithSchema(const Aws::String & value)1220 inline GetMLModelResult& WithSchema(const Aws::String& value) { SetSchema(value); return *this;} 1221 1222 /** 1223 * <p>The schema used by all of the data files referenced by the 1224 * <code>DataSource</code>.</p> <p> <b>Note:</b> This parameter is provided as part 1225 * of the verbose format.</p> 1226 */ WithSchema(Aws::String && value)1227 inline GetMLModelResult& WithSchema(Aws::String&& value) { SetSchema(std::move(value)); return *this;} 1228 1229 /** 1230 * <p>The schema used by all of the data files referenced by the 1231 * <code>DataSource</code>.</p> <p> <b>Note:</b> This parameter is provided as part 1232 * of the verbose format.</p> 1233 */ WithSchema(const char * value)1234 inline GetMLModelResult& WithSchema(const char* value) { SetSchema(value); return *this;} 1235 1236 private: 1237 1238 Aws::String m_mLModelId; 1239 1240 Aws::String m_trainingDataSourceId; 1241 1242 Aws::String m_createdByIamUser; 1243 1244 Aws::Utils::DateTime m_createdAt; 1245 1246 Aws::Utils::DateTime m_lastUpdatedAt; 1247 1248 Aws::String m_name; 1249 1250 EntityStatus m_status; 1251 1252 long long m_sizeInBytes; 1253 1254 RealtimeEndpointInfo m_endpointInfo; 1255 1256 Aws::Map<Aws::String, Aws::String> m_trainingParameters; 1257 1258 Aws::String m_inputDataLocationS3; 1259 1260 MLModelType m_mLModelType; 1261 1262 double m_scoreThreshold; 1263 1264 Aws::Utils::DateTime m_scoreThresholdLastUpdatedAt; 1265 1266 Aws::String m_logUri; 1267 1268 Aws::String m_message; 1269 1270 long long m_computeTime; 1271 1272 Aws::Utils::DateTime m_finishedAt; 1273 1274 Aws::Utils::DateTime m_startedAt; 1275 1276 Aws::String m_recipe; 1277 1278 Aws::String m_schema; 1279 }; 1280 1281 } // namespace Model 1282 } // namespace MachineLearning 1283 } // namespace Aws 1284