createSolution method

Future<CreateSolutionResponse> createSolution({
  1. required String datasetGroupArn,
  2. required String name,
  3. String? eventType,
  4. bool? performAutoML,
  5. bool? performHPO,
  6. String? recipeArn,
  7. SolutionConfig? solutionConfig,
})

Creates the configuration for training a model. A trained model is known as a solution. After the configuration is created, you train the model (create a solution) by calling the CreateSolutionVersion operation. Every time you call CreateSolutionVersion, a new version of the solution is created.

After creating a solution version, you check its accuracy by calling GetSolutionMetrics. When you are satisfied with the version, you deploy it using CreateCampaign. The campaign provides recommendations to a client through the GetRecommendations API.

To train a model, Amazon Personalize requires training data and a recipe. The training data comes from the dataset group that you provide in the request. A recipe specifies the training algorithm and a feature transformation. You can specify one of the predefined recipes provided by Amazon Personalize. Alternatively, you can specify performAutoML and Amazon Personalize will analyze your data and select the optimum USER_PERSONALIZATION recipe for you.

Status

A solution can be in one of the following states:

  • CREATE PENDING > CREATE IN_PROGRESS > ACTIVE -or- CREATE FAILED
  • DELETE PENDING > DELETE IN_PROGRESS
To get the status of the solution, call DescribeSolution. Wait until the status shows as ACTIVE before calling CreateSolutionVersion.

Related APIs

May throw InvalidInputException. May throw ResourceAlreadyExistsException. May throw ResourceNotFoundException. May throw LimitExceededException. May throw ResourceInUseException.

Parameter datasetGroupArn : The Amazon Resource Name (ARN) of the dataset group that provides the training data.

Parameter name : The name for the solution.

Parameter eventType : When your have multiple event types (using an EVENT_TYPE schema field), this parameter specifies which event type (for example, 'click' or 'like') is used for training the model.

Parameter performAutoML : Whether to perform automated machine learning (AutoML). The default is false. For this case, you must specify recipeArn.

When set to true, Amazon Personalize analyzes your training data and selects the optimal USER_PERSONALIZATION recipe and hyperparameters. In this case, you must omit recipeArn. Amazon Personalize determines the optimal recipe by running tests with different values for the hyperparameters. AutoML lengthens the training process as compared to selecting a specific recipe.

Parameter performHPO : Whether to perform hyperparameter optimization (HPO) on the specified or selected recipe. The default is false.

When performing AutoML, this parameter is always true and you should not set it to false.

Parameter recipeArn : The ARN of the recipe to use for model training. Only specified when performAutoML is false.

Parameter solutionConfig : The configuration to use with the solution. When performAutoML is set to true, Amazon Personalize only evaluates the autoMLConfig section of the solution configuration.

Implementation

Future<CreateSolutionResponse> createSolution({
  required String datasetGroupArn,
  required String name,
  String? eventType,
  bool? performAutoML,
  bool? performHPO,
  String? recipeArn,
  SolutionConfig? solutionConfig,
}) async {
  ArgumentError.checkNotNull(datasetGroupArn, 'datasetGroupArn');
  _s.validateStringLength(
    'datasetGroupArn',
    datasetGroupArn,
    0,
    256,
    isRequired: true,
  );
  ArgumentError.checkNotNull(name, 'name');
  _s.validateStringLength(
    'name',
    name,
    1,
    63,
    isRequired: true,
  );
  _s.validateStringLength(
    'eventType',
    eventType,
    0,
    256,
  );
  _s.validateStringLength(
    'recipeArn',
    recipeArn,
    0,
    256,
  );
  final headers = <String, String>{
    'Content-Type': 'application/x-amz-json-1.1',
    'X-Amz-Target': 'AmazonPersonalize.CreateSolution'
  };
  final jsonResponse = await _protocol.send(
    method: 'POST',
    requestUri: '/',
    exceptionFnMap: _exceptionFns,
    // TODO queryParams
    headers: headers,
    payload: {
      'datasetGroupArn': datasetGroupArn,
      'name': name,
      if (eventType != null) 'eventType': eventType,
      if (performAutoML != null) 'performAutoML': performAutoML,
      if (performHPO != null) 'performHPO': performHPO,
      if (recipeArn != null) 'recipeArn': recipeArn,
      if (solutionConfig != null) 'solutionConfig': solutionConfig,
    },
  );

  return CreateSolutionResponse.fromJson(jsonResponse.body);
}