1package Paws::MachineLearning { 2 use Moose; 3 sub service { 'machinelearning' } 4 sub version { '2014-12-12' } 5 sub target_prefix { 'AmazonML_20141212' } 6 sub json_version { "1.1" } 7 8 with 'Paws::API::Caller', 'Paws::API::EndpointResolver', 'Paws::Net::V4Signature', 'Paws::Net::JsonCaller', 'Paws::Net::JsonResponse'; 9 10 11 sub CreateBatchPrediction { 12 my $self = shift; 13 my $call_object = $self->new_with_coercions('Paws::MachineLearning::CreateBatchPrediction', @_); 14 return $self->caller->do_call($self, $call_object); 15 } 16 sub CreateDataSourceFromRDS { 17 my $self = shift; 18 my $call_object = $self->new_with_coercions('Paws::MachineLearning::CreateDataSourceFromRDS', @_); 19 return $self->caller->do_call($self, $call_object); 20 } 21 sub CreateDataSourceFromRedshift { 22 my $self = shift; 23 my $call_object = $self->new_with_coercions('Paws::MachineLearning::CreateDataSourceFromRedshift', @_); 24 return $self->caller->do_call($self, $call_object); 25 } 26 sub CreateDataSourceFromS3 { 27 my $self = shift; 28 my $call_object = $self->new_with_coercions('Paws::MachineLearning::CreateDataSourceFromS3', @_); 29 return $self->caller->do_call($self, $call_object); 30 } 31 sub CreateEvaluation { 32 my $self = shift; 33 my $call_object = $self->new_with_coercions('Paws::MachineLearning::CreateEvaluation', @_); 34 return $self->caller->do_call($self, $call_object); 35 } 36 sub CreateMLModel { 37 my $self = shift; 38 my $call_object = $self->new_with_coercions('Paws::MachineLearning::CreateMLModel', @_); 39 return $self->caller->do_call($self, $call_object); 40 } 41 sub CreateRealtimeEndpoint { 42 my $self = shift; 43 my $call_object = $self->new_with_coercions('Paws::MachineLearning::CreateRealtimeEndpoint', @_); 44 return $self->caller->do_call($self, $call_object); 45 } 46 sub DeleteBatchPrediction { 47 my $self = shift; 48 my $call_object = $self->new_with_coercions('Paws::MachineLearning::DeleteBatchPrediction', @_); 49 return $self->caller->do_call($self, $call_object); 50 } 51 sub DeleteDataSource { 52 my $self = shift; 53 my $call_object = $self->new_with_coercions('Paws::MachineLearning::DeleteDataSource', @_); 54 return $self->caller->do_call($self, $call_object); 55 } 56 sub DeleteEvaluation { 57 my $self = shift; 58 my $call_object = $self->new_with_coercions('Paws::MachineLearning::DeleteEvaluation', @_); 59 return $self->caller->do_call($self, $call_object); 60 } 61 sub DeleteMLModel { 62 my $self = shift; 63 my $call_object = $self->new_with_coercions('Paws::MachineLearning::DeleteMLModel', @_); 64 return $self->caller->do_call($self, $call_object); 65 } 66 sub DeleteRealtimeEndpoint { 67 my $self = shift; 68 my $call_object = $self->new_with_coercions('Paws::MachineLearning::DeleteRealtimeEndpoint', @_); 69 return $self->caller->do_call($self, $call_object); 70 } 71 sub DescribeBatchPredictions { 72 my $self = shift; 73 my $call_object = $self->new_with_coercions('Paws::MachineLearning::DescribeBatchPredictions', @_); 74 return $self->caller->do_call($self, $call_object); 75 } 76 sub DescribeDataSources { 77 my $self = shift; 78 my $call_object = $self->new_with_coercions('Paws::MachineLearning::DescribeDataSources', @_); 79 return $self->caller->do_call($self, $call_object); 80 } 81 sub DescribeEvaluations { 82 my $self = shift; 83 my $call_object = $self->new_with_coercions('Paws::MachineLearning::DescribeEvaluations', @_); 84 return $self->caller->do_call($self, $call_object); 85 } 86 sub DescribeMLModels { 87 my $self = shift; 88 my $call_object = $self->new_with_coercions('Paws::MachineLearning::DescribeMLModels', @_); 89 return $self->caller->do_call($self, $call_object); 90 } 91 sub GetBatchPrediction { 92 my $self = shift; 93 my $call_object = $self->new_with_coercions('Paws::MachineLearning::GetBatchPrediction', @_); 94 return $self->caller->do_call($self, $call_object); 95 } 96 sub GetDataSource { 97 my $self = shift; 98 my $call_object = $self->new_with_coercions('Paws::MachineLearning::GetDataSource', @_); 99 return $self->caller->do_call($self, $call_object); 100 } 101 sub GetEvaluation { 102 my $self = shift; 103 my $call_object = $self->new_with_coercions('Paws::MachineLearning::GetEvaluation', @_); 104 return $self->caller->do_call($self, $call_object); 105 } 106 sub GetMLModel { 107 my $self = shift; 108 my $call_object = $self->new_with_coercions('Paws::MachineLearning::GetMLModel', @_); 109 return $self->caller->do_call($self, $call_object); 110 } 111 sub Predict { 112 my $self = shift; 113 my $call_object = $self->new_with_coercions('Paws::MachineLearning::Predict', @_); 114 return $self->caller->do_call($self, $call_object); 115 } 116 sub UpdateBatchPrediction { 117 my $self = shift; 118 my $call_object = $self->new_with_coercions('Paws::MachineLearning::UpdateBatchPrediction', @_); 119 return $self->caller->do_call($self, $call_object); 120 } 121 sub UpdateDataSource { 122 my $self = shift; 123 my $call_object = $self->new_with_coercions('Paws::MachineLearning::UpdateDataSource', @_); 124 return $self->caller->do_call($self, $call_object); 125 } 126 sub UpdateEvaluation { 127 my $self = shift; 128 my $call_object = $self->new_with_coercions('Paws::MachineLearning::UpdateEvaluation', @_); 129 return $self->caller->do_call($self, $call_object); 130 } 131 sub UpdateMLModel { 132 my $self = shift; 133 my $call_object = $self->new_with_coercions('Paws::MachineLearning::UpdateMLModel', @_); 134 return $self->caller->do_call($self, $call_object); 135 } 136} 1371; 138 139### main pod documentation begin ### 140 141=head1 NAME 142 143Paws::MachineLearning - Perl Interface to AWS Amazon Machine Learning 144 145=head1 SYNOPSIS 146 147 use Paws; 148 149 my $obj = Paws->service('MachineLearning')->new; 150 my $res = $obj->Method( 151 Arg1 => $val1, 152 Arg2 => [ 'V1', 'V2' ], 153 # if Arg3 is an object, the HashRef will be used as arguments to the constructor 154 # of the arguments type 155 Arg3 => { Att1 => 'Val1' }, 156 # if Arg4 is an array of objects, the HashRefs will be passed as arguments to 157 # the constructor of the arguments type 158 Arg4 => [ { Att1 => 'Val1' }, { Att1 => 'Val2' } ], 159 ); 160 161=head1 DESCRIPTION 162 163 164 165Definition of the public APIs exposed by Amazon Machine Learning 166 167 168 169 170 171 172 173 174 175 176=head1 METHODS 177 178=head2 CreateBatchPrediction(BatchPredictionDataSourceId => Str, BatchPredictionId => Str, MLModelId => Str, OutputUri => Str, [BatchPredictionName => Str]) 179 180Each argument is described in detail in: L<Paws::MachineLearning::CreateBatchPrediction> 181 182Returns: a L<Paws::MachineLearning::CreateBatchPredictionOutput> instance 183 184 185 186Generates predictions for a group of observations. The observations to 187process exist in one or more data files referenced by a C<DataSource>. 188This operation creates a new C<BatchPrediction>, and uses an C<MLModel> 189and the data files referenced by the C<DataSource> as information 190sources. 191 192C<CreateBatchPrediction> is an asynchronous operation. In response to 193C<CreateBatchPrediction>, Amazon Machine Learning (Amazon ML) 194immediately returns and sets the C<BatchPrediction> status to 195C<PENDING>. After the C<BatchPrediction> completes, Amazon ML sets the 196status to C<COMPLETED>. 197 198You can poll for status updates by using the GetBatchPrediction 199operation and checking the C<Status> parameter of the result. After the 200C<COMPLETED> status appears, the results are available in the location 201specified by the C<OutputUri> parameter. 202 203 204 205 206 207 208 209 210 211 212 213=head2 CreateDataSourceFromRDS(DataSourceId => Str, RDSData => Paws::MachineLearning::RDSDataSpec, RoleARN => Str, [ComputeStatistics => Bool, DataSourceName => Str]) 214 215Each argument is described in detail in: L<Paws::MachineLearning::CreateDataSourceFromRDS> 216 217Returns: a L<Paws::MachineLearning::CreateDataSourceFromRDSOutput> instance 218 219 220 221Creates a C<DataSource> object from an Amazon Relational Database 222Service (Amazon RDS). A C<DataSource> references data that can be used 223to perform CreateMLModel, CreateEvaluation, or CreateBatchPrediction 224operations. 225 226C<CreateDataSourceFromRDS> is an asynchronous operation. In response to 227C<CreateDataSourceFromRDS>, Amazon Machine Learning (Amazon ML) 228immediately returns and sets the C<DataSource> status to C<PENDING>. 229After the C<DataSource> is created and ready for use, Amazon ML sets 230the C<Status> parameter to C<COMPLETED>. C<DataSource> in C<COMPLETED> 231or C<PENDING> status can only be used to perform CreateMLModel, 232CreateEvaluation, or CreateBatchPrediction operations. 233 234If Amazon ML cannot accept the input source, it sets the C<Status> 235parameter to C<FAILED> and includes an error message in the C<Message> 236attribute of the GetDataSource operation response. 237 238 239 240 241 242 243 244 245 246 247 248=head2 CreateDataSourceFromRedshift(DataSourceId => Str, DataSpec => Paws::MachineLearning::RedshiftDataSpec, RoleARN => Str, [ComputeStatistics => Bool, DataSourceName => Str]) 249 250Each argument is described in detail in: L<Paws::MachineLearning::CreateDataSourceFromRedshift> 251 252Returns: a L<Paws::MachineLearning::CreateDataSourceFromRedshiftOutput> instance 253 254 255 256Creates a C<DataSource> from Amazon Redshift. A C<DataSource> 257references data that can be used to perform either CreateMLModel, 258CreateEvaluation or CreateBatchPrediction operations. 259 260C<CreateDataSourceFromRedshift> is an asynchronous operation. In 261response to C<CreateDataSourceFromRedshift>, Amazon Machine Learning 262(Amazon ML) immediately returns and sets the C<DataSource> status to 263C<PENDING>. After the C<DataSource> is created and ready for use, 264Amazon ML sets the C<Status> parameter to C<COMPLETED>. C<DataSource> 265in C<COMPLETED> or C<PENDING> status can only be used to perform 266CreateMLModel, CreateEvaluation, or CreateBatchPrediction operations. 267 268If Amazon ML cannot accept the input source, it sets the C<Status> 269parameter to C<FAILED> and includes an error message in the C<Message> 270attribute of the GetDataSource operation response. 271 272The observations should exist in the database hosted on an Amazon 273Redshift cluster and should be specified by a C<SelectSqlQuery>. Amazon 274ML executes Unload command in Amazon Redshift to transfer the result 275set of C<SelectSqlQuery> to C<S3StagingLocation.> 276 277After the C<DataSource> is created, it's ready for use in evaluations 278and batch predictions. If you plan to use the C<DataSource> to train an 279C<MLModel>, the C<DataSource> requires another item -- a recipe. A 280recipe describes the observation variables that participate in training 281an C<MLModel>. A recipe describes how each input variable will be used 282in training. Will the variable be included or excluded from training? 283Will the variable be manipulated, for example, combined with another 284variable or split apart into word combinations? The recipe provides 285answers to these questions. For more information, see the Amazon 286Machine Learning Developer Guide. 287 288 289 290 291 292 293 294 295 296 297 298=head2 CreateDataSourceFromS3(DataSourceId => Str, DataSpec => Paws::MachineLearning::S3DataSpec, [ComputeStatistics => Bool, DataSourceName => Str]) 299 300Each argument is described in detail in: L<Paws::MachineLearning::CreateDataSourceFromS3> 301 302Returns: a L<Paws::MachineLearning::CreateDataSourceFromS3Output> instance 303 304 305 306Creates a C<DataSource> object. A C<DataSource> references data that 307can be used to perform CreateMLModel, CreateEvaluation, or 308CreateBatchPrediction operations. 309 310C<CreateDataSourceFromS3> is an asynchronous operation. In response to 311C<CreateDataSourceFromS3>, Amazon Machine Learning (Amazon ML) 312immediately returns and sets the C<DataSource> status to C<PENDING>. 313After the C<DataSource> is created and ready for use, Amazon ML sets 314the C<Status> parameter to C<COMPLETED>. C<DataSource> in C<COMPLETED> 315or C<PENDING> status can only be used to perform CreateMLModel, 316CreateEvaluation or CreateBatchPrediction operations. 317 318If Amazon ML cannot accept the input source, it sets the C<Status> 319parameter to C<FAILED> and includes an error message in the C<Message> 320attribute of the GetDataSource operation response. 321 322The observation data used in a C<DataSource> should be ready to use; 323that is, it should have a consistent structure, and missing data values 324should be kept to a minimum. The observation data must reside in one or 325more CSV files in an Amazon Simple Storage Service (Amazon S3) bucket, 326along with a schema that describes the data items by name and type. The 327same schema must be used for all of the data files referenced by the 328C<DataSource>. 329 330After the C<DataSource> has been created, it's ready to use in 331evaluations and batch predictions. If you plan to use the C<DataSource> 332to train an C<MLModel>, the C<DataSource> requires another item: a 333recipe. A recipe describes the observation variables that participate 334in training an C<MLModel>. A recipe describes how each input variable 335will be used in training. Will the variable be included or excluded 336from training? Will the variable be manipulated, for example, combined 337with another variable, or split apart into word combinations? The 338recipe provides answers to these questions. For more information, see 339the Amazon Machine Learning Developer Guide. 340 341 342 343 344 345 346 347 348 349 350 351=head2 CreateEvaluation(EvaluationDataSourceId => Str, EvaluationId => Str, MLModelId => Str, [EvaluationName => Str]) 352 353Each argument is described in detail in: L<Paws::MachineLearning::CreateEvaluation> 354 355Returns: a L<Paws::MachineLearning::CreateEvaluationOutput> instance 356 357 358 359Creates a new C<Evaluation> of an C<MLModel>. An C<MLModel> is 360evaluated on a set of observations associated to a C<DataSource>. Like 361a C<DataSource> for an C<MLModel>, the C<DataSource> for an 362C<Evaluation> contains values for the Target Variable. The 363C<Evaluation> compares the predicted result for each observation to the 364actual outcome and provides a summary so that you know how effective 365the C<MLModel> functions on the test data. Evaluation generates a 366relevant performance metric such as BinaryAUC, RegressionRMSE or 367MulticlassAvgFScore based on the corresponding C<MLModelType>: 368C<BINARY>, C<REGRESSION> or C<MULTICLASS>. 369 370C<CreateEvaluation> is an asynchronous operation. In response to 371C<CreateEvaluation>, Amazon Machine Learning (Amazon ML) immediately 372returns and sets the evaluation status to C<PENDING>. After the 373C<Evaluation> is created and ready for use, Amazon ML sets the status 374to C<COMPLETED>. 375 376You can use the GetEvaluation operation to check progress of the 377evaluation during the creation operation. 378 379 380 381 382 383 384 385 386 387 388 389=head2 CreateMLModel(MLModelId => Str, MLModelType => Str, TrainingDataSourceId => Str, [MLModelName => Str, Parameters => Paws::MachineLearning::TrainingParameters, Recipe => Str, RecipeUri => Str]) 390 391Each argument is described in detail in: L<Paws::MachineLearning::CreateMLModel> 392 393Returns: a L<Paws::MachineLearning::CreateMLModelOutput> instance 394 395 396 397Creates a new C<MLModel> using the data files and the recipe as 398information sources. 399 400An C<MLModel> is nearly immutable. Users can only update the 401C<MLModelName> and the C<ScoreThreshold> in an C<MLModel> without 402creating a new C<MLModel>. 403 404C<CreateMLModel> is an asynchronous operation. In response to 405C<CreateMLModel>, Amazon Machine Learning (Amazon ML) immediately 406returns and sets the C<MLModel> status to C<PENDING>. After the 407C<MLModel> is created and ready for use, Amazon ML sets the status to 408C<COMPLETED>. 409 410You can use the GetMLModel operation to check progress of the 411C<MLModel> during the creation operation. 412 413CreateMLModel requires a C<DataSource> with computed statistics, which 414can be created by setting C<ComputeStatistics> to C<true> in 415CreateDataSourceFromRDS, CreateDataSourceFromS3, or 416CreateDataSourceFromRedshift operations. 417 418 419 420 421 422 423 424 425 426 427 428=head2 CreateRealtimeEndpoint(MLModelId => Str) 429 430Each argument is described in detail in: L<Paws::MachineLearning::CreateRealtimeEndpoint> 431 432Returns: a L<Paws::MachineLearning::CreateRealtimeEndpointOutput> instance 433 434 435 436Creates a real-time endpoint for the C<MLModel>. The endpoint contains 437the URI of the C<MLModel>; that is, the location to send real-time 438prediction requests for the specified C<MLModel>. 439 440 441 442 443 444 445 446 447 448 449 450=head2 DeleteBatchPrediction(BatchPredictionId => Str) 451 452Each argument is described in detail in: L<Paws::MachineLearning::DeleteBatchPrediction> 453 454Returns: a L<Paws::MachineLearning::DeleteBatchPredictionOutput> instance 455 456 457 458Assigns the DELETED status to a C<BatchPrediction>, rendering it 459unusable. 460 461After using the C<DeleteBatchPrediction> operation, you can use the 462GetBatchPrediction operation to verify that the status of the 463C<BatchPrediction> changed to DELETED. 464 465The result of the C<DeleteBatchPrediction> operation is irreversible. 466 467 468 469 470 471 472 473 474 475 476 477=head2 DeleteDataSource(DataSourceId => Str) 478 479Each argument is described in detail in: L<Paws::MachineLearning::DeleteDataSource> 480 481Returns: a L<Paws::MachineLearning::DeleteDataSourceOutput> instance 482 483 484 485Assigns the DELETED status to a C<DataSource>, rendering it unusable. 486 487After using the C<DeleteDataSource> operation, you can use the 488GetDataSource operation to verify that the status of the C<DataSource> 489changed to DELETED. 490 491The results of the C<DeleteDataSource> operation are irreversible. 492 493 494 495 496 497 498 499 500 501 502 503=head2 DeleteEvaluation(EvaluationId => Str) 504 505Each argument is described in detail in: L<Paws::MachineLearning::DeleteEvaluation> 506 507Returns: a L<Paws::MachineLearning::DeleteEvaluationOutput> instance 508 509 510 511Assigns the C<DELETED> status to an C<Evaluation>, rendering it 512unusable. 513 514After invoking the C<DeleteEvaluation> operation, you can use the 515GetEvaluation operation to verify that the status of the C<Evaluation> 516changed to C<DELETED>. 517 518The results of the C<DeleteEvaluation> operation are irreversible. 519 520 521 522 523 524 525 526 527 528 529 530=head2 DeleteMLModel(MLModelId => Str) 531 532Each argument is described in detail in: L<Paws::MachineLearning::DeleteMLModel> 533 534Returns: a L<Paws::MachineLearning::DeleteMLModelOutput> instance 535 536 537 538Assigns the DELETED status to an C<MLModel>, rendering it unusable. 539 540After using the C<DeleteMLModel> operation, you can use the GetMLModel 541operation to verify that the status of the C<MLModel> changed to 542DELETED. 543 544The result of the C<DeleteMLModel> operation is irreversible. 545 546 547 548 549 550 551 552 553 554 555 556=head2 DeleteRealtimeEndpoint(MLModelId => Str) 557 558Each argument is described in detail in: L<Paws::MachineLearning::DeleteRealtimeEndpoint> 559 560Returns: a L<Paws::MachineLearning::DeleteRealtimeEndpointOutput> instance 561 562 563 564Deletes a real time endpoint of an C<MLModel>. 565 566 567 568 569 570 571 572 573 574 575 576=head2 DescribeBatchPredictions([EQ => Str, FilterVariable => Str, GE => Str, GT => Str, LE => Str, Limit => Int, LT => Str, NE => Str, NextToken => Str, Prefix => Str, SortOrder => Str]) 577 578Each argument is described in detail in: L<Paws::MachineLearning::DescribeBatchPredictions> 579 580Returns: a L<Paws::MachineLearning::DescribeBatchPredictionsOutput> instance 581 582 583 584Returns a list of C<BatchPrediction> operations that match the search 585criteria in the request. 586 587 588 589 590 591 592 593 594 595 596 597=head2 DescribeDataSources([EQ => Str, FilterVariable => Str, GE => Str, GT => Str, LE => Str, Limit => Int, LT => Str, NE => Str, NextToken => Str, Prefix => Str, SortOrder => Str]) 598 599Each argument is described in detail in: L<Paws::MachineLearning::DescribeDataSources> 600 601Returns: a L<Paws::MachineLearning::DescribeDataSourcesOutput> instance 602 603 604 605Returns a list of C<DataSource> that match the search criteria in the 606request. 607 608 609 610 611 612 613 614 615 616 617 618=head2 DescribeEvaluations([EQ => Str, FilterVariable => Str, GE => Str, GT => Str, LE => Str, Limit => Int, LT => Str, NE => Str, NextToken => Str, Prefix => Str, SortOrder => Str]) 619 620Each argument is described in detail in: L<Paws::MachineLearning::DescribeEvaluations> 621 622Returns: a L<Paws::MachineLearning::DescribeEvaluationsOutput> instance 623 624 625 626Returns a list of C<DescribeEvaluations> that match the search criteria 627in the request. 628 629 630 631 632 633 634 635 636 637 638 639=head2 DescribeMLModels([EQ => Str, FilterVariable => Str, GE => Str, GT => Str, LE => Str, Limit => Int, LT => Str, NE => Str, NextToken => Str, Prefix => Str, SortOrder => Str]) 640 641Each argument is described in detail in: L<Paws::MachineLearning::DescribeMLModels> 642 643Returns: a L<Paws::MachineLearning::DescribeMLModelsOutput> instance 644 645 646 647Returns a list of C<MLModel> that match the search criteria in the 648request. 649 650 651 652 653 654 655 656 657 658 659 660=head2 GetBatchPrediction(BatchPredictionId => Str) 661 662Each argument is described in detail in: L<Paws::MachineLearning::GetBatchPrediction> 663 664Returns: a L<Paws::MachineLearning::GetBatchPredictionOutput> instance 665 666 667 668Returns a C<BatchPrediction> that includes detailed metadata, status, 669and data file information for a C<Batch Prediction> request. 670 671 672 673 674 675 676 677 678 679 680 681=head2 GetDataSource(DataSourceId => Str, [Verbose => Bool]) 682 683Each argument is described in detail in: L<Paws::MachineLearning::GetDataSource> 684 685Returns: a L<Paws::MachineLearning::GetDataSourceOutput> instance 686 687 688 689Returns a C<DataSource> that includes metadata and data file 690information, as well as the current status of the C<DataSource>. 691 692C<GetDataSource> provides results in normal or verbose format. The 693verbose format adds the schema description and the list of files 694pointed to by the DataSource to the normal format. 695 696 697 698 699 700 701 702 703 704 705 706=head2 GetEvaluation(EvaluationId => Str) 707 708Each argument is described in detail in: L<Paws::MachineLearning::GetEvaluation> 709 710Returns: a L<Paws::MachineLearning::GetEvaluationOutput> instance 711 712 713 714Returns an C<Evaluation> that includes metadata as well as the current 715status of the C<Evaluation>. 716 717 718 719 720 721 722 723 724 725 726 727=head2 GetMLModel(MLModelId => Str, [Verbose => Bool]) 728 729Each argument is described in detail in: L<Paws::MachineLearning::GetMLModel> 730 731Returns: a L<Paws::MachineLearning::GetMLModelOutput> instance 732 733 734 735Returns an C<MLModel> that includes detailed metadata, and data source 736information as well as the current status of the C<MLModel>. 737 738C<GetMLModel> provides results in normal or verbose format. 739 740 741 742 743 744 745 746 747 748 749 750=head2 Predict(MLModelId => Str, PredictEndpoint => Str, Record => Paws::MachineLearning::Record) 751 752Each argument is described in detail in: L<Paws::MachineLearning::Predict> 753 754Returns: a L<Paws::MachineLearning::PredictOutput> instance 755 756 757 758Generates a prediction for the observation using the specified 759C<MLModel>. 760 761Not all response parameters will be populated because this is dependent 762on the type of requested model. 763 764 765 766 767 768 769 770 771 772 773 774=head2 UpdateBatchPrediction(BatchPredictionId => Str, BatchPredictionName => Str) 775 776Each argument is described in detail in: L<Paws::MachineLearning::UpdateBatchPrediction> 777 778Returns: a L<Paws::MachineLearning::UpdateBatchPredictionOutput> instance 779 780 781 782Updates the C<BatchPredictionName> of a C<BatchPrediction>. 783 784You can use the GetBatchPrediction operation to view the contents of 785the updated data element. 786 787 788 789 790 791 792 793 794 795 796 797=head2 UpdateDataSource(DataSourceId => Str, DataSourceName => Str) 798 799Each argument is described in detail in: L<Paws::MachineLearning::UpdateDataSource> 800 801Returns: a L<Paws::MachineLearning::UpdateDataSourceOutput> instance 802 803 804 805Updates the C<DataSourceName> of a C<DataSource>. 806 807You can use the GetDataSource operation to view the contents of the 808updated data element. 809 810 811 812 813 814 815 816 817 818 819 820=head2 UpdateEvaluation(EvaluationId => Str, EvaluationName => Str) 821 822Each argument is described in detail in: L<Paws::MachineLearning::UpdateEvaluation> 823 824Returns: a L<Paws::MachineLearning::UpdateEvaluationOutput> instance 825 826 827 828Updates the C<EvaluationName> of an C<Evaluation>. 829 830You can use the GetEvaluation operation to view the contents of the 831updated data element. 832 833 834 835 836 837 838 839 840 841 842 843=head2 UpdateMLModel(MLModelId => Str, [MLModelName => Str, ScoreThreshold => Num]) 844 845Each argument is described in detail in: L<Paws::MachineLearning::UpdateMLModel> 846 847Returns: a L<Paws::MachineLearning::UpdateMLModelOutput> instance 848 849 850 851Updates the C<MLModelName> and the C<ScoreThreshold> of an C<MLModel>. 852 853You can use the GetMLModel operation to view the contents of the 854updated data element. 855 856 857 858 859 860 861 862 863 864 865 866=head1 SEE ALSO 867 868This service class forms part of L<Paws> 869 870=head1 BUGS and CONTRIBUTIONS 871 872The source code is located here: https://github.com/pplu/aws-sdk-perl 873 874Please report bugs to: https://github.com/pplu/aws-sdk-perl/issues 875 876=cut 877 878