---
title: Getting Started with Datadog
description: Datadog, the leading service for cloud-scale monitoring.
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---

# aws_personalize_solution{% #aws_personalize_solution %}

## `account_id`{% #account_id %}

**Type**: `STRING`

## `auto_ml_result`{% #auto_ml_result %}

**Type**: `STRUCT`**Provider name**: `autoMLResult`**Description**: When `performAutoML` is true, specifies the best recipe found.

- `best_recipe_arn`**Type**: `STRING`**Provider name**: `bestRecipeArn`**Description**: The Amazon Resource Name (ARN) of the best recipe.

## `creation_date_time`{% #creation_date_time %}

**Type**: `TIMESTAMP`**Provider name**: `creationDateTime`**Description**: The creation date and time (in Unix time) of the solution.

## `dataset_group_arn`{% #dataset_group_arn %}

**Type**: `STRING`**Provider name**: `datasetGroupArn`**Description**: The Amazon Resource Name (ARN) of the dataset group that provides the training data.

## `event_type`{% #event_type %}

**Type**: `STRING`**Provider name**: `eventType`**Description**: The event type (for example, 'click' or 'like') that is used for training the model. If no `eventType` is provided, Amazon Personalize uses all interactions for training with equal weight regardless of type.

## `last_updated_date_time`{% #last_updated_date_time %}

**Type**: `TIMESTAMP`**Provider name**: `lastUpdatedDateTime`**Description**: The date and time (in Unix time) that the solution was last updated.

## `latest_solution_update`{% #latest_solution_update %}

**Type**: `STRUCT`**Provider name**: `latestSolutionUpdate`**Description**: Provides a summary of the latest updates to the solution.

- `creation_date_time`**Type**: `TIMESTAMP`**Provider name**: `creationDateTime`**Description**: The date and time (in Unix format) that the solution update was created.
- `failure_reason`**Type**: `STRING`**Provider name**: `failureReason`**Description**: If a solution update fails, the reason behind the failure.
- `last_updated_date_time`**Type**: `TIMESTAMP`**Provider name**: `lastUpdatedDateTime`**Description**: The date and time (in Unix time) that the solution update was last updated.
- `perform_auto_training`**Type**: `BOOLEAN`**Provider name**: `performAutoTraining`**Description**: Whether the solution automatically creates solution versions.
- `perform_incremental_update`**Type**: `BOOLEAN`**Provider name**: `performIncrementalUpdate`**Description**: A Boolean value that indicates whether incremental training updates are performed on the model. When enabled, this allows the model to learn from new data more frequently without requiring full retraining, which enables near real-time personalization. This parameter is supported only for solutions that use the semantic-similarity recipe.
- `solution_update_config`**Type**: `STRUCT`**Provider name**: `solutionUpdateConfig`**Description**: The configuration details of the solution.
  - `auto_training_config`**Type**: `STRUCT`**Provider name**: `autoTrainingConfig`
    - `scheduling_expression`**Type**: `STRING`**Provider name**: `schedulingExpression`**Description**: Specifies how often to automatically train new solution versions. Specify a rate expression in rate(value unit) format. For value, specify a number between 1 and 30. For unit, specify `day` or `days`. For example, to automatically create a new solution version every 5 days, specify `rate(5 days)`. The default is every 7 days. For more information about auto training, see [Creating and configuring a solution](https://docs.aws.amazon.com/personalize/latest/dg/customizing-solution-config.html).
  - `events_config`**Type**: `STRUCT`**Provider name**: `eventsConfig`**Description**: Describes the configuration of an event, which includes a list of event parameters. You can specify up to 10 event parameters. Events are used in solution creation.
    - `event_parameters_list`**Type**: `UNORDERED_LIST_STRUCT`**Provider name**: `eventParametersList`**Description**: A list of event parameters, which includes event types and their event value thresholds and weights.
      - `event_type`**Type**: `STRING`**Provider name**: `eventType`**Description**: The name of the event type to be considered for solution creation.
      - `event_value_threshold`**Type**: `DOUBLE`**Provider name**: `eventValueThreshold`**Description**: The threshold of the event type. Only events with a value greater or equal to this threshold will be considered for solution creation.
      - `weight`**Type**: `DOUBLE`**Provider name**: `weight`**Description**: The weight of the event type. A higher weight means higher importance of the event type for the created solution.
- `status`**Type**: `STRING`**Provider name**: `status`**Description**: The status of the solution update. A solution update can be in one of the following states: CREATE PENDING > CREATE IN_PROGRESS > ACTIVE -or- CREATE FAILED

## `latest_solution_version`{% #latest_solution_version %}

**Type**: `STRUCT`**Provider name**: `latestSolutionVersion`**Description**: Describes the latest version of the solution, including the status and the ARN.

- `creation_date_time`**Type**: `TIMESTAMP`**Provider name**: `creationDateTime`**Description**: The date and time (in Unix time) that this version of a solution was created.
- `failure_reason`**Type**: `STRING`**Provider name**: `failureReason`**Description**: If a solution version fails, the reason behind the failure.
- `last_updated_date_time`**Type**: `TIMESTAMP`**Provider name**: `lastUpdatedDateTime`**Description**: The date and time (in Unix time) that the solution version was last updated.
- `solution_version_arn`**Type**: `STRING`**Provider name**: `solutionVersionArn`**Description**: The Amazon Resource Name (ARN) of the solution version.
- `status`**Type**: `STRING`**Provider name**: `status`**Description**: The status of the solution version. A solution version can be in one of the following states:
  - CREATE PENDING > CREATE IN_PROGRESS > ACTIVE -or- CREATE FAILED
- `training_mode`**Type**: `STRING`**Provider name**: `trainingMode`**Description**: The scope of training to be performed when creating the solution version. A `FULL` training considers all of the data in your dataset group. An `UPDATE` processes only the data that has changed since the latest training. Only solution versions created with the User-Personalization recipe can use `UPDATE`.
- `training_type`**Type**: `STRING`**Provider name**: `trainingType`**Description**: Whether the solution version was created automatically or manually.

## `name`{% #name %}

**Type**: `STRING`**Provider name**: `name`**Description**: The name of the solution.

## `perform_auto_ml`{% #perform_auto_ml %}

**Type**: `BOOLEAN`**Provider name**: `performAutoML`**Description**:We don't recommend enabling automated machine learning. Instead, match your use case to the available Amazon Personalize recipes. For more information, see [Determining your use case.](https://docs.aws.amazon.com/personalize/latest/dg/determining-use-case.html)When true, Amazon Personalize performs a search for the best USER_PERSONALIZATION recipe from the list specified in the solution configuration (`recipeArn` must not be specified). When false (the default), Amazon Personalize uses `recipeArn` for training.

## `perform_auto_training`{% #perform_auto_training %}

**Type**: `BOOLEAN`**Provider name**: `performAutoTraining`**Description**: Specifies whether the solution automatically creates solution versions. The default is `True` and the solution automatically creates new solution versions every 7 days. For more information about auto training, see [Creating and configuring a solution](https://docs.aws.amazon.com/personalize/latest/dg/customizing-solution-config.html).

## `perform_hpo`{% #perform_hpo %}

**Type**: `BOOLEAN`**Provider name**: `performHPO`**Description**: Whether to perform hyperparameter optimization (HPO) on the chosen recipe. The default is `false`.

## `perform_incremental_update`{% #perform_incremental_update %}

**Type**: `BOOLEAN`**Provider name**: `performIncrementalUpdate`**Description**: A Boolean value that indicates whether incremental training updates are performed on the model. When enabled, this allows the model to learn from new data more frequently without requiring full retraining, which enables near real-time personalization. This parameter is supported only for solutions that use the semantic-similarity recipe

## `recipe_arn`{% #recipe_arn %}

**Type**: `STRING`**Provider name**: `recipeArn`**Description**: The ARN of the recipe used to create the solution. This is required when `performAutoML` is false.

## `solution_arn`{% #solution_arn %}

**Type**: `STRING`**Provider name**: `solutionArn`**Description**: The ARN of the solution.

## `solution_config`{% #solution_config %}

**Type**: `STRUCT`**Provider name**: `solutionConfig`**Description**: Describes the configuration properties for the solution.

- `algorithm_hyper_parameters`**Type**: `MAP_STRING_STRING`**Provider name**: `algorithmHyperParameters`**Description**: Lists the algorithm hyperparameters and their values.
- `auto_ml_config`**Type**: `STRUCT`**Provider name**: `autoMLConfig`**Description**: The [AutoMLConfig](https://docs.aws.amazon.com/personalize/latest/dg/API_AutoMLConfig.html) object containing a list of recipes to search when AutoML is performed.
  - `metric_name`**Type**: `STRING`**Provider name**: `metricName`**Description**: The metric to optimize.
  - `recipe_list`**Type**: `UNORDERED_LIST_STRING`**Provider name**: `recipeList`**Description**: The list of candidate recipes.
- `auto_training_config`**Type**: `STRUCT`**Provider name**: `autoTrainingConfig`**Description**: Specifies the automatic training configuration to use.
  - `scheduling_expression`**Type**: `STRING`**Provider name**: `schedulingExpression`**Description**: Specifies how often to automatically train new solution versions. Specify a rate expression in rate(value unit) format. For value, specify a number between 1 and 30. For unit, specify `day` or `days`. For example, to automatically create a new solution version every 5 days, specify `rate(5 days)`. The default is every 7 days. For more information about auto training, see [Creating and configuring a solution](https://docs.aws.amazon.com/personalize/latest/dg/customizing-solution-config.html).
- `event_value_threshold`**Type**: `STRING`**Provider name**: `eventValueThreshold`**Description**: Only events with a value greater than or equal to this threshold are used for training a model.
- `events_config`**Type**: `STRUCT`**Provider name**: `eventsConfig`**Description**: Describes the configuration of an event, which includes a list of event parameters. You can specify up to 10 event parameters. Events are used in solution creation.
  - `event_parameters_list`**Type**: `UNORDERED_LIST_STRUCT`**Provider name**: `eventParametersList`**Description**: A list of event parameters, which includes event types and their event value thresholds and weights.
    - `event_type`**Type**: `STRING`**Provider name**: `eventType`**Description**: The name of the event type to be considered for solution creation.
    - `event_value_threshold`**Type**: `DOUBLE`**Provider name**: `eventValueThreshold`**Description**: The threshold of the event type. Only events with a value greater or equal to this threshold will be considered for solution creation.
    - `weight`**Type**: `DOUBLE`**Provider name**: `weight`**Description**: The weight of the event type. A higher weight means higher importance of the event type for the created solution.
- `feature_transformation_parameters`**Type**: `MAP_STRING_STRING`**Provider name**: `featureTransformationParameters`**Description**: Lists the feature transformation parameters.
- `hpo_config`**Type**: `STRUCT`**Provider name**: `hpoConfig`**Description**: Describes the properties for hyperparameter optimization (HPO).
  - `algorithm_hyper_parameter_ranges`**Type**: `STRUCT`**Provider name**: `algorithmHyperParameterRanges`**Description**: The hyperparameters and their allowable ranges.
    - `categorical_hyper_parameter_ranges`**Type**: `UNORDERED_LIST_STRUCT`**Provider name**: `categoricalHyperParameterRanges`**Description**: The categorical hyperparameters and their ranges.
      - `name`**Type**: `STRING`**Provider name**: `name`**Description**: The name of the hyperparameter.
      - `values`**Type**: `UNORDERED_LIST_STRING`**Provider name**: `values`**Description**: A list of the categories for the hyperparameter.
    - `continuous_hyper_parameter_ranges`**Type**: `UNORDERED_LIST_STRUCT`**Provider name**: `continuousHyperParameterRanges`**Description**: The continuous hyperparameters and their ranges.
      - `max_value`**Type**: `DOUBLE`**Provider name**: `maxValue`**Description**: The maximum allowable value for the hyperparameter.
      - `min_value`**Type**: `DOUBLE`**Provider name**: `minValue`**Description**: The minimum allowable value for the hyperparameter.
      - `name`**Type**: `STRING`**Provider name**: `name`**Description**: The name of the hyperparameter.
    - `integer_hyper_parameter_ranges`**Type**: `UNORDERED_LIST_STRUCT`**Provider name**: `integerHyperParameterRanges`**Description**: The integer-valued hyperparameters and their ranges.
      - `max_value`**Type**: `INT32`**Provider name**: `maxValue`**Description**: The maximum allowable value for the hyperparameter.
      - `min_value`**Type**: `INT32`**Provider name**: `minValue`**Description**: The minimum allowable value for the hyperparameter.
      - `name`**Type**: `STRING`**Provider name**: `name`**Description**: The name of the hyperparameter.
  - `hpo_objective`**Type**: `STRUCT`**Provider name**: `hpoObjective`**Description**: The metric to optimize during HPO.Amazon Personalize doesn't support configuring the `hpoObjective` at this time.
    - `metric_name`**Type**: `STRING`**Provider name**: `metricName`**Description**: The name of the metric.
    - `metric_regex`**Type**: `STRING`**Provider name**: `metricRegex`**Description**: A regular expression for finding the metric in the training job logs.
    - `type`**Type**: `STRING`**Provider name**: `type`**Description**: The type of the metric. Valid values are `Maximize` and `Minimize`.
  - `hpo_resource_config`**Type**: `STRUCT`**Provider name**: `hpoResourceConfig`**Description**: Describes the resource configuration for HPO.
    - `max_number_of_training_jobs`**Type**: `STRING`**Provider name**: `maxNumberOfTrainingJobs`**Description**: The maximum number of training jobs when you create a solution version. The maximum value for `maxNumberOfTrainingJobs` is `40`.
    - `max_parallel_training_jobs`**Type**: `STRING`**Provider name**: `maxParallelTrainingJobs`**Description**: The maximum number of parallel training jobs when you create a solution version. The maximum value for `maxParallelTrainingJobs` is `10`.
- `optimization_objective`**Type**: `STRUCT`**Provider name**: `optimizationObjective`**Description**: Describes the additional objective for the solution, such as maximizing streaming minutes or increasing revenue. For more information see [Optimizing a solution](https://docs.aws.amazon.com/personalize/latest/dg/optimizing-solution-for-objective.html).
  - `item_attribute`**Type**: `STRING`**Provider name**: `itemAttribute`**Description**: The numerical metadata column in an Items dataset related to the optimization objective. For example, VIDEO_LENGTH (to maximize streaming minutes), or PRICE (to maximize revenue).
  - `objective_sensitivity`**Type**: `STRING`**Provider name**: `objectiveSensitivity`**Description**: Specifies how Amazon Personalize balances the importance of your optimization objective versus relevance.
- `training_data_config`**Type**: `STRUCT`**Provider name**: `trainingDataConfig`**Description**: Specifies the training data configuration to use when creating a custom solution version (trained model).
  - `excluded_dataset_columns`**Type**: `STRING`**Provider name**: `excludedDatasetColumns`**Description**: Specifies the columns to exclude from training. Each key is a dataset type, and each value is a list of columns. Exclude columns to control what data Amazon Personalize uses to generate recommendations. For example, you might have a column that you want to use only to filter recommendations. You can exclude this column from training and Amazon Personalize considers it only when filtering.
  - `included_dataset_columns`**Type**: `STRING`**Provider name**: `includedDatasetColumns`**Description**: A map that specifies which columns to include from each dataset during training. The map can contain up to 3 entries, where each key is a dataset name (maximum length of 256 characters, must contain only letters and underscores) and each value is an array of up to 50 column names. Column names can be up to 150 characters long, must start with a letter or underscore, and can contain only letters, numbers, and underscores.

## `status`{% #status %}

**Type**: `STRING`**Provider name**: `status`**Description**: The status of the solution. A solution can be in one of the following states:

- CREATE PENDING > CREATE IN_PROGRESS > ACTIVE -or- CREATE FAILED
- DELETE PENDING > DELETE IN_PROGRESS



## `tags`{% #tags %}

**Type**: `UNORDERED_LIST_STRING`