Smaws_Client_Personalize.Typestype nonrec update_solution_response = {solution_arn : arn option;The same solution Amazon Resource Name (ARN) as given in the request.
*)}type nonrec auto_training_config = {scheduling_expression : scheduling_expression option;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.
*)}The automatic training configuration to use when performAutoTraining is true.
type nonrec event_parameters = {weight : event_type_weight option;The weight of the event type. A higher weight means higher importance of the event type for the created solution.
*)event_value_threshold : event_type_threshold_value option;The threshold of the event type. Only events with a value greater or equal to this threshold will be considered for solution creation.
*)event_type : event_type option;The name of the event type to be considered for solution creation.
*)}Describes the parameters of events, which are used in solution creation.
type nonrec event_parameters_list = event_parameters listtype nonrec events_config = {event_parameters_list : event_parameters_list option;A list of event parameters, which includes event types and their event value thresholds and weights.
*)}Describes the configuration of events, which are used in solution creation.
type nonrec solution_update_config = {events_config : events_config option;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.
*)auto_training_config : auto_training_config option;}The configuration details of the solution update.
type nonrec update_solution_request = {solution_update_config : solution_update_config option;The new configuration details of the solution.
*)perform_incremental_update : perform_incremental_update option;Whether to perform incremental training updates on your 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.
*)perform_auto_training : perform_auto_training option;Whether the solution uses automatic training to create new solution versions (trained models). You can change the training frequency by specifying a schedulingExpression in the AutoTrainingConfig as part of solution configuration.
If you turn on automatic training, the first automatic training starts within one hour after the solution update completes. If you manually create a solution version within the hour, the solution skips the first automatic training. For more information about automatic training, see Configuring automatic training.
After training starts, you can get the solution version's Amazon Resource Name (ARN) with the ListSolutionVersions API operation. To get its status, use the DescribeSolutionVersion.
*)solution_arn : arn;The Amazon Resource Name (ARN) of the solution to update.
*)}Could not find the specified resource.
The specified resource is in use.
The limit on the number of requests per second has been exceeded.
Provide a valid value for the field or parameter.
type nonrec update_recommender_response = {recommender_arn : arn option;The same recommender Amazon Resource Name (ARN) as given in the request.
*)}type nonrec hyper_parameters = (parameter_name * parameter_value) listtype nonrec column_names_list = column_name listtype nonrec excluded_dataset_columns = (dataset_type * column_names_list) listtype nonrec included_dataset_columns = (dataset_type * column_names_list) listtype nonrec training_data_config = {included_dataset_columns : included_dataset_columns option;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.
*)excluded_dataset_columns : excluded_dataset_columns option;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.
*)}The training data configuration to use when creating a domain recommender or custom solution version (trained model).
type nonrec recommender_config = {enable_metadata_with_recommendations : boolean_ option;Whether metadata with recommendations is enabled for the recommender. If enabled, you can specify the columns from your Items dataset in your request for recommendations. Amazon Personalize returns this data for each item in the recommendation response. For information about enabling metadata for a recommender, see Enabling metadata in recommendations for a recommender.
If you enable metadata in recommendations, you will incur additional costs. For more information, see Amazon Personalize pricing.
*)training_data_config : training_data_config option;Specifies the training data configuration to use when creating a domain recommender.
*)min_recommendation_requests_per_second : transactions_per_second option;Specifies the requested minimum provisioned recommendation requests per second that Amazon Personalize will support. A high minRecommendationRequestsPerSecond will increase your bill. We recommend starting with 1 for minRecommendationRequestsPerSecond (the default). Track your usage using Amazon CloudWatch metrics, and increase the minRecommendationRequestsPerSecond as necessary.
item_exploration_config : hyper_parameters option;Specifies the exploration configuration hyperparameters, including explorationWeight and explorationItemAgeCutOff, you want to use to configure the amount of item exploration Amazon Personalize uses when recommending items. Provide itemExplorationConfig data only if your recommenders generate personalized recommendations for a user (not popular items or similar items).
}The configuration details of the recommender.
type nonrec update_recommender_request = {recommender_config : recommender_config;The configuration details of the recommender.
*)recommender_arn : arn;The Amazon Resource Name (ARN) of the recommender to modify.
*)}type nonrec update_metric_attribution_response = {metric_attribution_arn : arn option;The Amazon Resource Name (ARN) for the metric attribution that you updated.
*)}type nonrec metric_attribute = {expression : metric_expression;The attribute's expression. Available functions are SUM() or SAMPLECOUNT(). For SUM() functions, provide the dataset type (either Interactions or Items) and column to sum as a parameter. For example SUM(Items.PRICE).
metric_name : metric_name;The metric's name. The name helps you identify the metric in Amazon CloudWatch or Amazon S3.
*)event_type : event_type;The metric's event type.
*)}Contains information on a metric that a metric attribution reports on. For more information, see Measuring impact of recommendations.
type nonrec metric_attributes = metric_attribute listtype nonrec metric_attributes_names_list = metric_name listtype nonrec s3_data_config = {kms_key_arn : kms_key_arn option;The Amazon Resource Name (ARN) of the Key Management Service (KMS) key that Amazon Personalize uses to encrypt or decrypt the input and output files.
*)path : s3_location;The file path of the Amazon S3 bucket.
*)}The configuration details of an Amazon S3 input or output bucket.
type nonrec metric_attribution_output = {role_arn : role_arn;The Amazon Resource Name (ARN) of the IAM service role that has permissions to add data to your output Amazon S3 bucket and add metrics to Amazon CloudWatch. For more information, see Measuring impact of recommendations.
*)s3_data_destination : s3_data_config option;}The output configuration details for a metric attribution.
type nonrec update_metric_attribution_request = {metric_attribution_arn : arn option;The Amazon Resource Name (ARN) for the metric attribution to update.
*)metrics_output_config : metric_attribution_output option;An output config for the metric attribution.
*)remove_metrics : metric_attributes_names_list option;Remove metric attributes from the metric attribution.
*)add_metrics : metric_attributes option;Add new metric attributes to the metric attribution.
*)}The specified resource already exists.
type nonrec update_dataset_response = {dataset_arn : arn option;The Amazon Resource Name (ARN) of the dataset you updated.
*)}type nonrec update_campaign_response = {campaign_arn : arn option;The same campaign ARN as given in the request.
*)}type nonrec ranking_influence =
(ranking_influence_type * ranking_influence_weight) listtype nonrec campaign_config = {ranking_influence : ranking_influence option;A map of ranking influence values for POPULARITY and FRESHNESS. For each key, specify a numerical value between 0.0 and 1.0 that determines how much influence that ranking factor has on the final recommendations. A value closer to 1.0 gives more weight to the factor, while a value closer to 0.0 reduces its influence.
*)sync_with_latest_solution_version : boolean_ option;Whether the campaign automatically updates to use the latest solution version (trained model) of a solution. If you specify True, you must specify the ARN of your solution for the SolutionVersionArn parameter. It must be in SolutionArn/$LATEST format. The default is False and you must manually update the campaign to deploy the latest solution version.
For more information about automatic campaign updates, see Enabling automatic campaign updates.
*)enable_metadata_with_recommendations : boolean_ option;Whether metadata with recommendations is enabled for the campaign. If enabled, you can specify the columns from your Items dataset in your request for recommendations. Amazon Personalize returns this data for each item in the recommendation response. For information about enabling metadata for a campaign, see Enabling metadata in recommendations for a campaign.
If you enable metadata in recommendations, you will incur additional costs. For more information, see Amazon Personalize pricing.
*)item_exploration_config : hyper_parameters option;Specifies the exploration configuration hyperparameters, including explorationWeight and explorationItemAgeCutOff, you want to use to configure the amount of item exploration Amazon Personalize uses when recommending items. Provide itemExplorationConfig data only if your solution uses the User-Personalization recipe.
}The configuration details of a campaign.
type nonrec update_campaign_request = {campaign_config : campaign_config option;The configuration details of a campaign.
*)min_provisioned_tp_s : transactions_per_second option;Specifies the requested minimum provisioned transactions (recommendations) per second that Amazon Personalize will support. A high minProvisionedTPS will increase your bill. We recommend starting with 1 for minProvisionedTPS (the default). Track your usage using Amazon CloudWatch metrics, and increase the minProvisionedTPS as necessary.
solution_version_arn : arn option;The Amazon Resource Name (ARN) of a new model to deploy. To specify the latest solution version of your solution, specify the ARN of your solution in SolutionArn/$LATEST format. You must use this format if you set syncWithLatestSolutionVersion to True in the CampaignConfig.
To deploy a model that isn't the latest solution version of your solution, specify the ARN of the solution version.
For more information about automatic campaign updates, see Enabling automatic campaign updates.
*)campaign_arn : arn;The Amazon Resource Name (ARN) of the campaign.
*)}type nonrec tag_keys = tag_key listThe request contains more tag keys than can be associated with a resource (50 tag keys per resource).
type nonrec tuned_hpo_params = {algorithm_hyper_parameters : hyper_parameters option;A list of the hyperparameter values of the best performing model.
*)}If hyperparameter optimization (HPO) was performed, contains the hyperparameter values of the best performing model.
You have exceeded the maximum number of tags you can apply to this resource.
type nonrec fields_for_theme_generation = {item_name : column_name;The name of the Items dataset column that stores the name of each item in the dataset.
*)}A string to string map of the configuration details for theme generation.
type nonrec theme_generation_config = {fields_for_theme_generation : fields_for_theme_generation;Fields used to generate descriptive themes for a batch inference job.
*)}The configuration details for generating themes with a batch inference job.
type nonrec tag = {tag_value : tag_value;The optional part of a key-value pair that makes up a tag. A value acts as a descriptor within a tag category (key).
*)tag_key : tag_key;One part of a key-value pair that makes up a tag. A key is a general label that acts like a category for more specific tag values.
*)}The optional metadata that you apply to resources to help you categorize and organize them. Each tag consists of a key and an optional value, both of which you define. For more information see Tagging Amazon Personalize resources.
type nonrec tags = tag listtype nonrec tag_resource_request = {resource_arn : arn;The resource's Amazon Resource Name (ARN).
*)}type nonrec stop_solution_version_creation_request = {solution_version_arn : arn;The Amazon Resource Name (ARN) of the solution version you want to stop creating.
*)}type nonrec stop_recommender_response = {recommender_arn : arn option;The Amazon Resource Name (ARN) of the recommender you stopped.
*)}type nonrec stop_recommender_request = {recommender_arn : arn;The Amazon Resource Name (ARN) of the recommender to stop.
*)}type nonrec start_recommender_response = {recommender_arn : arn option;The Amazon Resource Name (ARN) of the recommender you started.
*)}type nonrec start_recommender_request = {recommender_arn : arn;The Amazon Resource Name (ARN) of the recommender to start.
*)}type nonrec solution_summary = {recipe_arn : arn option;The Amazon Resource Name (ARN) of the recipe used by the solution.
*)last_updated_date_time : date option;The date and time (in Unix time) that the solution was last updated.
*)creation_date_time : date option;The date and time (in Unix time) that the solution was created.
*)status : status option;The status of the solution.
A solution can be in one of the following states:
solution_arn : arn option;The Amazon Resource Name (ARN) of the solution.
*)name : name option;The name of the solution.
*)}Provides a summary of the properties of a solution. For a complete listing, call the DescribeSolution API.
type nonrec solutions = solution_summary listtype nonrec solution_version_summary = {failure_reason : failure_reason option;If a solution version fails, the reason behind the failure.
*)last_updated_date_time : date option;The date and time (in Unix time) that the solution version was last updated.
*)creation_date_time : date option;The date and time (in Unix time) that this version of a solution was created.
*)training_type : training_type option;Whether the solution version was created automatically or manually.
*)training_mode : training_mode option;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.
status : status option;The status of the solution version.
A solution version can be in one of the following states:
solution_version_arn : arn option;The Amazon Resource Name (ARN) of the solution version.
*)}Provides a summary of the properties of a solution version. For a complete listing, call the DescribeSolutionVersion API.
type nonrec solution_versions = solution_version_summary listtype nonrec hpo_objective = {metric_regex : metric_regex option;A regular expression for finding the metric in the training job logs.
*)metric_name : metric_name option;The name of the metric.
*)type_ : hpo_objective_type option;The type of the metric. Valid values are Maximize and Minimize.
}The metric to optimize during hyperparameter optimization (HPO).
Amazon Personalize doesn't support configuring the hpoObjective at this time.
type nonrec hpo_resource_config = {max_parallel_training_jobs : hpo_resource option;The maximum number of parallel training jobs when you create a solution version. The maximum value for maxParallelTrainingJobs is 10.
max_number_of_training_jobs : hpo_resource option;The maximum number of training jobs when you create a solution version. The maximum value for maxNumberOfTrainingJobs is 40.
}Describes the resource configuration for hyperparameter optimization (HPO).
type nonrec integer_hyper_parameter_range = {max_value : integer_max_value option;The maximum allowable value for the hyperparameter.
*)min_value : integer_min_value option;The minimum allowable value for the hyperparameter.
*)name : parameter_name option;The name of the hyperparameter.
*)}Provides the name and range of an integer-valued hyperparameter.
type nonrec integer_hyper_parameter_ranges = integer_hyper_parameter_range listtype nonrec continuous_hyper_parameter_range = {max_value : continuous_max_value option;The maximum allowable value for the hyperparameter.
*)min_value : continuous_min_value option;The minimum allowable value for the hyperparameter.
*)name : parameter_name option;The name of the hyperparameter.
*)}Provides the name and range of a continuous hyperparameter.
type nonrec continuous_hyper_parameter_ranges =
continuous_hyper_parameter_range listtype nonrec categorical_values = categorical_value listtype nonrec categorical_hyper_parameter_range = {values : categorical_values option;A list of the categories for the hyperparameter.
*)name : parameter_name option;The name of the hyperparameter.
*)}Provides the name and range of a categorical hyperparameter.
type nonrec categorical_hyper_parameter_ranges =
categorical_hyper_parameter_range listtype nonrec hyper_parameter_ranges = {categorical_hyper_parameter_ranges : categorical_hyper_parameter_ranges option;The categorical hyperparameters and their ranges.
*)continuous_hyper_parameter_ranges : continuous_hyper_parameter_ranges option;The continuous hyperparameters and their ranges.
*)integer_hyper_parameter_ranges : integer_hyper_parameter_ranges option;The integer-valued hyperparameters and their ranges.
*)}Specifies the hyperparameters and their ranges. Hyperparameters can be categorical, continuous, or integer-valued.
type nonrec hpo_config = {algorithm_hyper_parameter_ranges : hyper_parameter_ranges option;The hyperparameters and their allowable ranges.
*)hpo_resource_config : hpo_resource_config option;Describes the resource configuration for HPO.
*)hpo_objective : hpo_objective option;The metric to optimize during HPO.
Amazon Personalize doesn't support configuring the hpoObjective at this time.
}Describes the properties for hyperparameter optimization (HPO).
type nonrec feature_transformation_parameters =
(parameter_name * parameter_value) listtype nonrec arn_list = arn listtype nonrec auto_ml_config = {recipe_list : arn_list option;The list of candidate recipes.
*)metric_name : metric_name option;The metric to optimize.
*)}When the solution performs AutoML (performAutoML is true in CreateSolution), Amazon Personalize determines which recipe, from the specified list, optimizes the given metric. Amazon Personalize then uses that recipe for the solution.
type nonrec optimization_objective = {objective_sensitivity : objective_sensitivity option;Specifies how Amazon Personalize balances the importance of your optimization objective versus relevance.
*)item_attribute : item_attribute option;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).
*)}Describes the additional objective for the solution, such as maximizing streaming minutes or increasing revenue. For more information see Optimizing a solution.
type nonrec solution_config = {auto_training_config : auto_training_config option;Specifies the automatic training configuration to use.
*)training_data_config : training_data_config option;Specifies the training data configuration to use when creating a custom solution version (trained model).
*)optimization_objective : optimization_objective option;Describes the additional objective for the solution, such as maximizing streaming minutes or increasing revenue. For more information see Optimizing a solution.
*)events_config : events_config option;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.
*)auto_ml_config : auto_ml_config option;feature_transformation_parameters : feature_transformation_parameters option;Lists the feature transformation parameters.
*)algorithm_hyper_parameters : hyper_parameters option;Lists the algorithm hyperparameters and their values.
*)hpo_config : hpo_config option;Describes the properties for hyperparameter optimization (HPO).
*)event_value_threshold : event_value_threshold option;Only events with a value greater than or equal to this threshold are used for training a model.
*)}Describes the configuration properties for the solution.
type nonrec solution_version = {training_type : training_type option;Whether the solution version was created automatically or manually.
*)last_updated_date_time : date option;The date and time (in Unix time) that the solution was last updated.
*)creation_date_time : date option;The date and time (in Unix time) that this version of the solution was created.
*)failure_reason : failure_reason option;If training a solution version fails, the reason for the failure.
*)status : status option;The status of the solution version.
A solution version can be in one of the following states:
tuned_hpo_params : tuned_hpo_params option;If hyperparameter optimization was performed, contains the hyperparameter values of the best performing model.
*)training_mode : training_mode option;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_hours : training_hours option;The time used to train the model. You are billed for the time it takes to train a model. This field is visible only after Amazon Personalize successfully trains a model.
*)solution_config : solution_config option;Describes the configuration properties for the solution.
*)dataset_group_arn : arn option;The Amazon Resource Name (ARN) of the dataset group providing the training data.
*)event_type : event_type option;The event type (for example, 'click' or 'like') that is used for training the model.
*)recipe_arn : arn option;The ARN of the recipe used in the solution.
*)perform_incremental_update : perform_incremental_update option;Whether the solution version should perform an incremental update. When set to true, the training will process only the data that has changed since the latest training, similar to when trainingMode is set to UPDATE. This can only be used with solution versions that use the User-Personalization recipe.
*)perform_auto_m_l : perform_auto_m_l option;When true, Amazon Personalize searches for the most optimal recipe according to the solution configuration. When false (the default), Amazon Personalize uses recipeArn.
perform_hp_o : perform_hp_o option;Whether to perform hyperparameter optimization (HPO) on the chosen recipe. The default is false.
solution_arn : arn option;The ARN of the solution.
*)solution_version_arn : arn option;The ARN of the solution version.
*)name : name option;The name of the solution version.
*)}An object that provides information about a specific version of a Solution in a Custom dataset group.
type nonrec solution_update_summary = {failure_reason : failure_reason option;If a solution update fails, the reason behind the failure.
*)last_updated_date_time : date option;The date and time (in Unix time) that the solution update was last updated.
*)creation_date_time : date option;The date and time (in Unix format) that the solution update was created.
*)perform_incremental_update : perform_incremental_update option;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.
*)perform_auto_training : perform_auto_training option;Whether the solution automatically creates solution versions.
*)status : status option;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
*)solution_update_config : solution_update_config option;The configuration details of the solution.
*)}Provides a summary of the properties of a solution update. For a complete listing, call the DescribeSolution API.
type nonrec auto_ml_result = {best_recipe_arn : arn option;The Amazon Resource Name (ARN) of the best recipe.
*)}When the solution performs AutoML (performAutoML is true in CreateSolution), specifies the recipe that best optimized the specified metric.
type nonrec solution = {latest_solution_update : solution_update_summary option;Provides a summary of the latest updates to the solution.
*)latest_solution_version : solution_version_summary option;Describes the latest version of the solution, including the status and the ARN.
*)last_updated_date_time : date option;The date and time (in Unix time) that the solution was last updated.
*)creation_date_time : date option;The creation date and time (in Unix time) of the solution.
*)status : status option;The status of the solution.
A solution can be in one of the following states:
auto_ml_result : auto_ml_result option;When performAutoML is true, specifies the best recipe found.
solution_config : solution_config option;Describes the configuration properties for the solution.
*)event_type : event_type option;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.
dataset_group_arn : arn option;The Amazon Resource Name (ARN) of the dataset group that provides the training data.
*)recipe_arn : arn option;The ARN of the recipe used to create the solution. This is required when performAutoML is false.
perform_incremental_update : perform_incremental_update option;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
*)perform_auto_training : perform_auto_training option;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.
*)perform_auto_m_l : perform_auto_m_l option;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.
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_hp_o : perform_hp_o option;Whether to perform hyperparameter optimization (HPO) on the chosen recipe. The default is false.
solution_arn : arn option;The ARN of the solution.
*)name : name option;The name of the solution.
*)}By default, all new solutions use automatic training. With automatic training, you incur training costs while your solution is active. To avoid unnecessary costs, when you are finished you can update the solution to turn off automatic training. For information about training costs, see Amazon Personalize pricing.
An object that provides information about a solution. A solution includes the custom recipe, customized parameters, and trained models (Solution Versions) that Amazon Personalize uses to generate recommendations.
After you create a solution, you can’t change its configuration. If you need to make changes, you can clone the solution with the Amazon Personalize console or create a new one.
type nonrec dataset_schema_summary = {domain : domain option;The domain of a schema that you created for a dataset in a Domain dataset group.
*)last_updated_date_time : date option;The date and time (in Unix time) that the schema was last updated.
*)creation_date_time : date option;The date and time (in Unix time) that the schema was created.
*)schema_arn : arn option;The Amazon Resource Name (ARN) of the schema.
*)name : name option;The name of the schema.
*)}Provides a summary of the properties of a dataset schema. For a complete listing, call the DescribeSchema API.
type nonrec schemas = dataset_schema_summary listtype nonrec resource_config = (parameter_name * parameter_value) listtype nonrec recommender_summary = {last_updated_date_time : date option;The date and time (in Unix format) that the recommender was last updated.
*)creation_date_time : date option;The date and time (in Unix format) that the recommender was created.
*)status : status option;The status of the recommender. A recommender can be in one of the following states:
recommender_config : recommender_config option;The configuration details of the recommender.
*)recipe_arn : arn option;The Amazon Resource Name (ARN) of the recipe (Domain dataset group use case) that the recommender was created for.
*)dataset_group_arn : arn option;The Amazon Resource Name (ARN) of the Domain dataset group that contains the recommender.
*)recommender_arn : arn option;The Amazon Resource Name (ARN) of the recommender.
*)name : name option;The name of the recommender.
*)}Provides a summary of the properties of the recommender.
type nonrec recommenders = recommender_summary listtype nonrec recommender_update_summary = {failure_reason : failure_reason option;If a recommender update fails, the reason behind the failure.
*)status : status option;The status of the recommender update. A recommender update can be in one of the following states:
CREATE PENDING > CREATE IN_PROGRESS > ACTIVE -or- CREATE FAILED
*)last_updated_date_time : date option;The date and time (in Unix time) that the recommender update was last updated.
*)creation_date_time : date option;The date and time (in Unix format) that the recommender update was created.
*)recommender_config : recommender_config option;The configuration details of the recommender update.
*)}Provides a summary of the properties of a recommender update. For a complete listing, call the DescribeRecommender API.
type nonrec metrics = (metric_name * metric_value) listtype nonrec recommender = {model_metrics : metrics option;Provides evaluation metrics that help you determine the performance of a recommender. For more information, see Evaluating a recommender.
*)latest_recommender_update : recommender_update_summary option;Provides a summary of the latest updates to the recommender.
*)failure_reason : failure_reason option;If a recommender fails, the reason behind the failure.
*)status : status option;The status of the recommender.
A recommender can be in one of the following states:
last_updated_date_time : date option;The date and time (in Unix format) that the recommender was last updated.
*)creation_date_time : date option;The date and time (in Unix format) that the recommender was created.
*)recommender_config : recommender_config option;The configuration details of the recommender.
*)recipe_arn : arn option;The Amazon Resource Name (ARN) of the recipe (Domain dataset group use case) that the recommender was created for.
*)name : name option;The name of the recommender.
*)dataset_group_arn : arn option;The Amazon Resource Name (ARN) of the Domain dataset group that contains the recommender.
*)recommender_arn : arn option;The Amazon Resource Name (ARN) of the recommender.
*)}Describes a recommendation generator for a Domain dataset group. You create a recommender in a Domain dataset group for a specific domain use case (domain recipe), and specify the recommender in a GetRecommendations request.
type nonrec recipe_summary = {domain : domain option;The domain of the recipe (if the recipe is a Domain dataset group use case).
*)last_updated_date_time : date option;The date and time (in Unix time) that the recipe was last updated.
*)creation_date_time : date option;The date and time (in Unix time) that the recipe was created.
*)status : status option;The status of the recipe.
*)recipe_arn : arn option;The Amazon Resource Name (ARN) of the recipe.
*)name : name option;The name of the recipe.
*)}Provides a summary of the properties of a recipe. For a complete listing, call the DescribeRecipe API.
type nonrec recipes = recipe_summary listtype nonrec recipe = {last_updated_date_time : date option;The date and time (in Unix format) that the recipe was last updated.
*)recipe_type : recipe_type option;One of the following values:
creation_date_time : date option;The date and time (in Unix format) that the recipe was created.
*)description : description option;The description of the recipe.
*)status : status option;The status of the recipe.
*)feature_transformation_arn : arn option;The ARN of the FeatureTransformation object.
*)algorithm_arn : arn option;The Amazon Resource Name (ARN) of the algorithm that Amazon Personalize uses to train the model.
*)recipe_arn : arn option;The Amazon Resource Name (ARN) of the recipe.
*)name : name option;The name of the recipe.
*)}Provides information about a recipe. Each recipe provides an algorithm that Amazon Personalize uses in model training when you use the CreateSolution operation.
type nonrec metric_attribution_summary = {failure_reason : failure_reason option;The metric attribution's failure reason.
*)last_updated_date_time : date option;The metric attribution's last updated date time.
*)creation_date_time : date option;The metric attribution's creation date time.
*)status : status option;The metric attribution's status.
*)metric_attribution_arn : arn option;The metric attribution's Amazon Resource Name (ARN).
*)name : name option;The name of the metric attribution.
*)}Provides a summary of the properties of a metric attribution. For a complete listing, call the DescribeMetricAttribution.
type nonrec metric_attributions = metric_attribution_summary listtype nonrec metric_attribution = {failure_reason : failure_reason option;The metric attribution's failure reason.
*)last_updated_date_time : date option;The metric attribution's last updated date time.
*)creation_date_time : date option;The metric attribution's creation date time.
*)status : status option;The metric attribution's status.
*)metrics_output_config : metric_attribution_output option;The metric attribution's output configuration.
*)dataset_group_arn : arn option;The metric attribution's dataset group Amazon Resource Name (ARN).
*)metric_attribution_arn : arn option;The metric attribution's Amazon Resource Name (ARN).
*)name : name option;The metric attribution's name.
*)}Contains information on a metric attribution. A metric attribution creates reports on the data that you import into Amazon Personalize. Depending on how you import the data, you can view reports in Amazon CloudWatch or Amazon S3. For more information, see Measuring impact of recommendations.
type nonrec list_solutions_response = {next_token : next_token option;A token for getting the next set of solutions (if they exist).
*)solutions : solutions option;A list of the current solutions.
*)}type nonrec list_solutions_request = {max_results : max_results option;The maximum number of solutions to return.
*)next_token : next_token option;A token returned from the previous call to ListSolutions for getting the next set of solutions (if they exist).
dataset_group_arn : arn option;The Amazon Resource Name (ARN) of the dataset group.
*)}The token is not valid.
type nonrec list_solution_versions_response = {next_token : next_token option;A token for getting the next set of solution versions (if they exist).
*)solution_versions : solution_versions option;A list of solution versions describing the version properties.
*)}type nonrec list_solution_versions_request = {max_results : max_results option;The maximum number of solution versions to return.
*)next_token : next_token option;A token returned from the previous call to ListSolutionVersions for getting the next set of solution versions (if they exist).
solution_arn : arn option;The Amazon Resource Name (ARN) of the solution.
*)}type nonrec list_schemas_response = {next_token : next_token option;A token used to get the next set of schemas (if they exist).
*)schemas : schemas option;A list of schemas.
*)}type nonrec list_schemas_request = {max_results : max_results option;The maximum number of schemas to return.
*)next_token : next_token option;A token returned from the previous call to ListSchemas for getting the next set of schemas (if they exist).
}type nonrec list_recommenders_response = {next_token : next_token option;A token for getting the next set of recommenders (if they exist).
*)recommenders : recommenders option;A list of the recommenders.
*)}type nonrec list_recommenders_request = {max_results : max_results option;The maximum number of recommenders to return.
*)next_token : next_token option;A token returned from the previous call to ListRecommenders for getting the next set of recommenders (if they exist).
dataset_group_arn : arn option;The Amazon Resource Name (ARN) of the Domain dataset group to list the recommenders for. When a Domain dataset group is not specified, all the recommenders associated with the account are listed.
*)}type nonrec list_recipes_response = {next_token : next_token option;A token for getting the next set of recipes.
*)recipes : recipes option;The list of available recipes.
*)}type nonrec list_recipes_request = {domain : domain option;Filters returned recipes by domain for a Domain dataset group. Only recipes (Domain dataset group use cases) for this domain are included in the response. If you don't specify a domain, all recipes are returned.
*)max_results : max_results option;The maximum number of recipes to return.
*)next_token : next_token option;A token returned from the previous call to ListRecipes for getting the next set of recipes (if they exist).
recipe_provider : recipe_provider option;The default is SERVICE.
}type nonrec list_metric_attributions_response = {next_token : next_token option;Specify the pagination token from a previous request to retrieve the next page of results.
*)metric_attributions : metric_attributions option;The list of metric attributions.
*)}type nonrec list_metric_attributions_request = {max_results : max_results option;The maximum number of metric attributions to return in one page of results.
*)next_token : next_token option;Specify the pagination token from a previous request to retrieve the next page of results.
*)dataset_group_arn : arn option;The metric attributions' dataset group Amazon Resource Name (ARN).
*)}type nonrec list_metric_attribution_metrics_response = {next_token : next_token option;Specify the pagination token from a previous ListMetricAttributionMetricsResponse request to retrieve the next page of results.
metrics : metric_attributes option;The metrics for the specified metric attribution.
*)}type nonrec list_metric_attribution_metrics_request = {max_results : max_results option;The maximum number of metrics to return in one page of results.
*)next_token : next_token option;Specify the pagination token from a previous request to retrieve the next page of results.
*)metric_attribution_arn : arn option;The Amazon Resource Name (ARN) of the metric attribution to retrieve attributes for.
*)}type nonrec filter_summary = {status : status option;The status of the filter.
*)failure_reason : failure_reason option;If the filter failed, the reason for the failure.
*)dataset_group_arn : arn option;The ARN of the dataset group to which the filter belongs.
*)last_updated_date_time : date option;The time at which the filter was last updated.
*)creation_date_time : date option;The time at which the filter was created.
*)filter_arn : arn option;The ARN of the filter.
*)name : name option;The name of the filter.
*)}A short summary of a filter's attributes.
type nonrec filters = filter_summary listtype nonrec list_filters_response = {next_token : next_token option;A token for getting the next set of filters (if they exist).
*)filters : filters option;A list of returned filters.
*)}type nonrec list_filters_request = {max_results : max_results option;The maximum number of filters to return.
*)next_token : next_token option;A token returned from the previous call to ListFilters for getting the next set of filters (if they exist).
dataset_group_arn : arn option;The ARN of the dataset group that contains the filters.
*)}type nonrec event_tracker_summary = {last_updated_date_time : date option;The date and time (in Unix time) that the event tracker was last updated.
*)creation_date_time : date option;The date and time (in Unix time) that the event tracker was created.
*)status : status option;The status of the event tracker.
An event tracker can be in one of the following states:
event_tracker_arn : arn option;The Amazon Resource Name (ARN) of the event tracker.
*)name : name option;The name of the event tracker.
*)}Provides a summary of the properties of an event tracker. For a complete listing, call the DescribeEventTracker API.
type nonrec event_trackers = event_tracker_summary listtype nonrec list_event_trackers_response = {next_token : next_token option;A token for getting the next set of event trackers (if they exist).
*)event_trackers : event_trackers option;A list of event trackers.
*)}type nonrec list_event_trackers_request = {max_results : max_results option;The maximum number of event trackers to return.
*)next_token : next_token option;A token returned from the previous call to ListEventTrackers for getting the next set of event trackers (if they exist).
dataset_group_arn : arn option;The ARN of a dataset group used to filter the response.
*)}type nonrec dataset_summary = {last_updated_date_time : date option;The date and time (in Unix time) that the dataset was last updated.
*)creation_date_time : date option;The date and time (in Unix time) that the dataset was created.
*)status : status option;The status of the dataset.
A dataset can be in one of the following states:
dataset_type : dataset_type option;The dataset type. One of the following values:
dataset_arn : arn option;The Amazon Resource Name (ARN) of the dataset.
*)name : name option;The name of the dataset.
*)}Provides a summary of the properties of a dataset. For a complete listing, call the DescribeDataset API.
type nonrec datasets = dataset_summary listtype nonrec list_datasets_response = {next_token : next_token option;A token for getting the next set of datasets (if they exist).
*)datasets : datasets option;An array of Dataset objects. Each object provides metadata information.
}type nonrec list_datasets_request = {max_results : max_results option;The maximum number of datasets to return.
*)next_token : next_token option;A token returned from the previous call to ListDatasets for getting the next set of dataset import jobs (if they exist).
dataset_group_arn : arn option;The Amazon Resource Name (ARN) of the dataset group that contains the datasets to list.
*)}type nonrec dataset_import_job_summary = {import_mode : import_mode option;The import mode the dataset import job used to update the data in the dataset. For more information see Updating existing bulk data.
*)failure_reason : failure_reason option;If a dataset import job fails, the reason behind the failure.
*)last_updated_date_time : date option;The date and time (in Unix time) that the dataset import job status was last updated.
*)creation_date_time : date option;The date and time (in Unix time) that the dataset import job was created.
*)status : status option;The status of the dataset import job.
A dataset import job can be in one of the following states:
job_name : name option;The name of the dataset import job.
*)dataset_import_job_arn : arn option;The Amazon Resource Name (ARN) of the dataset import job.
*)}Provides a summary of the properties of a dataset import job. For a complete listing, call the DescribeDatasetImportJob API.
type nonrec dataset_import_jobs = dataset_import_job_summary listtype nonrec list_dataset_import_jobs_response = {next_token : next_token option;A token for getting the next set of dataset import jobs (if they exist).
*)dataset_import_jobs : dataset_import_jobs option;The list of dataset import jobs.
*)}type nonrec list_dataset_import_jobs_request = {max_results : max_results option;The maximum number of dataset import jobs to return.
*)next_token : next_token option;A token returned from the previous call to ListDatasetImportJobs for getting the next set of dataset import jobs (if they exist).
dataset_arn : arn option;The Amazon Resource Name (ARN) of the dataset to list the dataset import jobs for.
*)}type nonrec dataset_group_summary = {domain : domain option;The domain of a Domain dataset group.
*)failure_reason : failure_reason option;If creating a dataset group fails, the reason behind the failure.
*)last_updated_date_time : date option;The date and time (in Unix time) that the dataset group was last updated.
*)creation_date_time : date option;The date and time (in Unix time) that the dataset group was created.
*)status : status option;The status of the dataset group.
A dataset group can be in one of the following states:
dataset_group_arn : arn option;The Amazon Resource Name (ARN) of the dataset group.
*)name : name option;The name of the dataset group.
*)}Provides a summary of the properties of a dataset group. For a complete listing, call the DescribeDatasetGroup API.
type nonrec dataset_groups = dataset_group_summary listtype nonrec list_dataset_groups_response = {next_token : next_token option;A token for getting the next set of dataset groups (if they exist).
*)dataset_groups : dataset_groups option;The list of your dataset groups.
*)}type nonrec list_dataset_groups_request = {max_results : max_results option;The maximum number of dataset groups to return.
*)next_token : next_token option;A token returned from the previous call to ListDatasetGroups for getting the next set of dataset groups (if they exist).
}type nonrec dataset_export_job_summary = {failure_reason : failure_reason option;If a dataset export job fails, the reason behind the failure.
*)last_updated_date_time : date option;The date and time (in Unix time) that the dataset export job status was last updated.
*)creation_date_time : date option;The date and time (in Unix time) that the dataset export job was created.
*)status : status option;The status of the dataset export job.
A dataset export job can be in one of the following states:
job_name : name option;The name of the dataset export job.
*)dataset_export_job_arn : arn option;The Amazon Resource Name (ARN) of the dataset export job.
*)}Provides a summary of the properties of a dataset export job. For a complete listing, call the DescribeDatasetExportJob API.
type nonrec dataset_export_jobs = dataset_export_job_summary listtype nonrec list_dataset_export_jobs_response = {next_token : next_token option;A token for getting the next set of dataset export jobs (if they exist).
*)dataset_export_jobs : dataset_export_jobs option;The list of dataset export jobs.
*)}type nonrec list_dataset_export_jobs_request = {max_results : max_results option;The maximum number of dataset export jobs to return.
*)next_token : next_token option;A token returned from the previous call to ListDatasetExportJobs for getting the next set of dataset export jobs (if they exist).
dataset_arn : arn option;The Amazon Resource Name (ARN) of the dataset to list the dataset export jobs for.
*)}type nonrec data_deletion_job_summary = {failure_reason : failure_reason option;If a data deletion job fails, provides the reason why.
*)last_updated_date_time : date option;The date and time (in Unix time) the data deletion job was last updated.
*)creation_date_time : date option;The creation date and time (in Unix time) of the data deletion job.
*)status : status option;The status of the data deletion job.
A data deletion job can have one of the following statuses:
job_name : name option;The name of the data deletion job.
*)dataset_group_arn : arn option;The Amazon Resource Name (ARN) of the dataset group the job deleted records from.
*)data_deletion_job_arn : arn option;The Amazon Resource Name (ARN) of the data deletion job.
*)}Provides a summary of the properties of a data deletion job. For a complete listing, call the DescribeDataDeletionJob API operation.
type nonrec data_deletion_jobs = data_deletion_job_summary listtype nonrec list_data_deletion_jobs_response = {next_token : next_token option;A token for getting the next set of data deletion jobs (if they exist).
*)data_deletion_jobs : data_deletion_jobs option;The list of data deletion jobs.
*)}type nonrec list_data_deletion_jobs_request = {max_results : max_results option;The maximum number of data deletion jobs to return.
*)next_token : next_token option;A token returned from the previous call to ListDataDeletionJobs for getting the next set of jobs (if they exist).
dataset_group_arn : arn option;The Amazon Resource Name (ARN) of the dataset group to list data deletion jobs for.
*)}type nonrec campaign_summary = {failure_reason : failure_reason option;If a campaign fails, the reason behind the failure.
*)last_updated_date_time : date option;The date and time (in Unix time) that the campaign was last updated.
*)creation_date_time : date option;The date and time (in Unix time) that the campaign was created.
*)status : status option;The status of the campaign.
A campaign can be in one of the following states:
campaign_arn : arn option;The Amazon Resource Name (ARN) of the campaign.
*)name : name option;The name of the campaign.
*)}Provides a summary of the properties of a campaign. For a complete listing, call the DescribeCampaign API.
type nonrec campaigns = campaign_summary listtype nonrec list_campaigns_response = {next_token : next_token option;A token for getting the next set of campaigns (if they exist).
*)campaigns : campaigns option;A list of the campaigns.
*)}type nonrec list_campaigns_request = {max_results : max_results option;The maximum number of campaigns to return.
*)next_token : next_token option;A token returned from the previous call to ListCampaigns for getting the next set of campaigns (if they exist).
*)solution_arn : arn option;The Amazon Resource Name (ARN) of the solution to list the campaigns for. When a solution is not specified, all the campaigns associated with the account are listed.
*)}type nonrec batch_segment_job_summary = {solution_version_arn : arn option;The Amazon Resource Name (ARN) of the solution version used by the batch segment job to generate batch segments.
*)failure_reason : failure_reason option;If the batch segment job failed, the reason for the failure.
*)last_updated_date_time : date option;The time at which the batch segment job was last updated.
*)creation_date_time : date option;The time at which the batch segment job was created.
*)status : status option;The status of the batch segment job. The status is one of the following values:
job_name : name option;The name of the batch segment job.
*)batch_segment_job_arn : arn option;The Amazon Resource Name (ARN) of the batch segment job.
*)}A truncated version of the BatchSegmentJob datatype. ListBatchSegmentJobs operation returns a list of batch segment job summaries.
type nonrec batch_segment_jobs = batch_segment_job_summary listtype nonrec list_batch_segment_jobs_response = {next_token : next_token option;The token to use to retrieve the next page of results. The value is null when there are no more results to return.
batch_segment_jobs : batch_segment_jobs option;A list containing information on each job that is returned.
*)}type nonrec list_batch_segment_jobs_request = {max_results : max_results option;The maximum number of batch segment job results to return in each page. The default value is 100.
*)next_token : next_token option;The token to request the next page of results.
*)solution_version_arn : arn option;The Amazon Resource Name (ARN) of the solution version that the batch segment jobs used to generate batch segments.
*)}type nonrec batch_inference_job_summary = {batch_inference_job_mode : batch_inference_job_mode option;The job's mode.
*)solution_version_arn : arn option;The ARN of the solution version used by the batch inference job.
*)failure_reason : failure_reason option;If the batch inference job failed, the reason for the failure.
*)last_updated_date_time : date option;The time at which the batch inference job was last updated.
*)creation_date_time : date option;The time at which the batch inference job was created.
*)status : status option;The status of the batch inference job. The status is one of the following values:
job_name : name option;The name of the batch inference job.
*)batch_inference_job_arn : arn option;The Amazon Resource Name (ARN) of the batch inference job.
*)}A truncated version of the BatchInferenceJob. The ListBatchInferenceJobs operation returns a list of batch inference job summaries.
type nonrec batch_inference_jobs = batch_inference_job_summary listtype nonrec list_batch_inference_jobs_response = {next_token : next_token option;The token to use to retrieve the next page of results. The value is null when there are no more results to return.
batch_inference_jobs : batch_inference_jobs option;A list containing information on each job that is returned.
*)}type nonrec list_batch_inference_jobs_request = {max_results : max_results option;The maximum number of batch inference job results to return in each page. The default value is 100.
*)next_token : next_token option;The token to request the next page of results.
*)solution_version_arn : arn option;The Amazon Resource Name (ARN) of the solution version from which the batch inference jobs were created.
*)}type nonrec get_solution_metrics_response = {metrics : metrics option;The metrics for the solution version. For more information, see Evaluating a solution version with metrics.
*)solution_version_arn : arn option;The same solution version ARN as specified in the request.
*)}type nonrec get_solution_metrics_request = {solution_version_arn : arn;The Amazon Resource Name (ARN) of the solution version for which to get metrics.
*)}type nonrec filter = {status : status option;The status of the filter.
*)filter_expression : filter_expression option;Specifies the type of item interactions to filter out of recommendation results. The filter expression must follow specific format rules. For information about filter expression structure and syntax, see Filter expressions.
*)failure_reason : failure_reason option;If the filter failed, the reason for its failure.
*)dataset_group_arn : arn option;The ARN of the dataset group to which the filter belongs.
*)last_updated_date_time : date option;The time at which the filter was last updated.
*)creation_date_time : date option;The time at which the filter was created.
*)filter_arn : arn option;The ARN of the filter.
*)name : name option;The name of the filter.
*)}Contains information on a recommendation filter, including its ARN, status, and filter expression.
type nonrec featurization_parameters = (parameter_name * parameter_value) listtype nonrec feature_transformation = {status : status option;The status of the feature transformation.
A feature transformation can be in one of the following states:
last_updated_date_time : date option;The last update date and time (in Unix time) of the feature transformation.
*)creation_date_time : date option;The creation date and time (in Unix time) of the feature transformation.
*)default_parameters : featurization_parameters option;Provides the default parameters for feature transformation.
*)feature_transformation_arn : arn option;The Amazon Resource Name (ARN) of the FeatureTransformation object.
*)name : name option;The name of the feature transformation.
*)}Provides feature transformation information. Feature transformation is the process of modifying raw input data into a form more suitable for model training.
type nonrec event_tracker = {last_updated_date_time : date option;The date and time (in Unix time) that the event tracker was last updated.
*)creation_date_time : date option;The date and time (in Unix format) that the event tracker was created.
*)status : status option;The status of the event tracker.
An event tracker can be in one of the following states:
dataset_group_arn : arn option;The Amazon Resource Name (ARN) of the dataset group that receives the event data.
*)tracking_id : tracking_id option;account_id : account_id option;The Amazon Web Services account that owns the event tracker.
*)event_tracker_arn : arn option;The ARN of the event tracker.
*)name : name option;The name of the event tracker.
*)}Provides information about an event tracker.
type nonrec describe_solution_version_response = {solution_version : solution_version option;The solution version.
*)}type nonrec describe_solution_version_request = {solution_version_arn : arn;The Amazon Resource Name (ARN) of the solution version.
*)}type nonrec describe_solution_response = {solution : solution option;An object that describes the solution.
*)}type nonrec describe_solution_request = {solution_arn : arn;The Amazon Resource Name (ARN) of the solution to describe.
*)}type nonrec dataset_schema = {domain : domain option;The domain of a schema that you created for a dataset in a Domain dataset group.
*)last_updated_date_time : date option;The date and time (in Unix time) that the schema was last updated.
*)creation_date_time : date option;The date and time (in Unix time) that the schema was created.
*)schema : avro_schema option;The schema.
*)schema_arn : arn option;The Amazon Resource Name (ARN) of the schema.
*)name : name option;The name of the schema.
*)}Describes the schema for a dataset. For more information on schemas, see CreateSchema.
type nonrec describe_schema_request = {schema_arn : arn;The Amazon Resource Name (ARN) of the schema to retrieve.
*)}type nonrec describe_recommender_response = {recommender : recommender option;The properties of the recommender.
*)}type nonrec describe_recommender_request = {recommender_arn : arn;The Amazon Resource Name (ARN) of the recommender to describe.
*)}type nonrec describe_recipe_response = {recipe : recipe option;An object that describes the recipe.
*)}type nonrec describe_recipe_request = {recipe_arn : arn;The Amazon Resource Name (ARN) of the recipe to describe.
*)}type nonrec describe_metric_attribution_response = {metric_attribution : metric_attribution option;The details of the metric attribution.
*)}type nonrec describe_metric_attribution_request = {metric_attribution_arn : arn;The metric attribution's Amazon Resource Name (ARN).
*)}type nonrec describe_feature_transformation_response = {feature_transformation : feature_transformation option;A listing of the FeatureTransformation properties.
*)}type nonrec describe_feature_transformation_request = {feature_transformation_arn : arn;The Amazon Resource Name (ARN) of the feature transformation to describe.
*)}type nonrec describe_event_tracker_response = {event_tracker : event_tracker option;An object that describes the event tracker.
*)}type nonrec describe_event_tracker_request = {event_tracker_arn : arn;The Amazon Resource Name (ARN) of the event tracker to describe.
*)}type nonrec dataset_update_summary = {last_updated_date_time : date option;The last update date and time (in Unix time) of the dataset.
*)creation_date_time : date option;The creation date and time (in Unix time) of the dataset update.
*)failure_reason : failure_reason option;If updating a dataset fails, provides the reason why.
*)status : status option;The status of the dataset update.
*)schema_arn : arn option;The Amazon Resource Name (ARN) of the schema that replaced the previous schema of the dataset.
*)}Describes an update to a dataset.
type nonrec dataset = {tracking_id : tracking_id option;The ID of the event tracker for an Action interactions dataset. You specify the tracker's ID in the PutActionInteractions API operation. Amazon Personalize uses it to direct new data to the Action interactions dataset in your dataset group.
latest_dataset_update : dataset_update_summary option;Describes the latest update to the dataset.
*)last_updated_date_time : date option;A time stamp that shows when the dataset was updated.
*)creation_date_time : date option;The creation date and time (in Unix time) of the dataset.
*)status : status option;The status of the dataset.
A dataset can be in one of the following states:
schema_arn : arn option;The ARN of the associated schema.
*)dataset_type : dataset_type option;One of the following values:
dataset_group_arn : arn option;The Amazon Resource Name (ARN) of the dataset group.
*)dataset_arn : arn option;The Amazon Resource Name (ARN) of the dataset that you want metadata for.
*)name : name option;The name of the dataset.
*)}Provides metadata for a dataset.
type nonrec describe_dataset_response = {dataset : dataset option;A listing of the dataset's properties.
*)}type nonrec describe_dataset_request = {dataset_arn : arn;The Amazon Resource Name (ARN) of the dataset to describe.
*)}type nonrec data_source = {data_location : s3_location option;For dataset import jobs, the path to the Amazon S3 bucket where the data that you want to upload to your dataset is stored. For data deletion jobs, the path to the Amazon S3 bucket that stores the list of records to delete.
For example:
s3://bucket-name/folder-name/fileName.csv
If your CSV files are in a folder in your Amazon S3 bucket and you want your import job or data deletion job to consider multiple files, you can specify the path to the folder. With a data deletion job, Amazon Personalize uses all files in the folder and any sub folder. Use the following syntax with a / after the folder name:
s3://bucket-name/folder-name/
}Describes the data source that contains the data to upload to a dataset, or the list of records to delete from Amazon Personalize.
type nonrec dataset_import_job = {publish_attribution_metrics_to_s3 : boolean_ option;Whether the job publishes metrics to Amazon S3 for a metric attribution.
*)import_mode : import_mode option;The import mode used by the dataset import job to import new records.
*)failure_reason : failure_reason option;If a dataset import job fails, provides the reason why.
*)last_updated_date_time : date option;The date and time (in Unix time) the dataset was last updated.
*)creation_date_time : date option;The creation date and time (in Unix time) of the dataset import job.
*)status : status option;The status of the dataset import job.
A dataset import job can be in one of the following states:
role_arn : arn option;The ARN of the IAM role that has permissions to read from the Amazon S3 data source.
*)data_source : data_source option;The Amazon S3 bucket that contains the training data to import.
*)dataset_arn : arn option;The Amazon Resource Name (ARN) of the dataset that receives the imported data.
*)dataset_import_job_arn : arn option;The ARN of the dataset import job.
*)job_name : name option;The name of the import job.
*)}Describes a job that imports training data from a data source (Amazon S3 bucket) to an Amazon Personalize dataset. For more information, see CreateDatasetImportJob.
A dataset import job can be in one of the following states:
type nonrec describe_dataset_import_job_response = {dataset_import_job : dataset_import_job option;Information about the dataset import job, including the status.
The status is one of the following values:
}type nonrec describe_dataset_import_job_request = {dataset_import_job_arn : arn;The Amazon Resource Name (ARN) of the dataset import job to describe.
*)}type nonrec dataset_group = {domain : domain option;The domain of a Domain dataset group.
*)failure_reason : failure_reason option;If creating a dataset group fails, provides the reason why.
*)last_updated_date_time : date option;The last update date and time (in Unix time) of the dataset group.
*)creation_date_time : date option;The creation date and time (in Unix time) of the dataset group.
*)kms_key_arn : kms_key_arn option;The Amazon Resource Name (ARN) of the Key Management Service (KMS) key used to encrypt the datasets.
*)role_arn : role_arn option;The ARN of the Identity and Access Management (IAM) role that has permissions to access the Key Management Service (KMS) key. Supplying an IAM role is only valid when also specifying a KMS key.
*)status : status option;The current status of the dataset group.
A dataset group can be in one of the following states:
dataset_group_arn : arn option;The Amazon Resource Name (ARN) of the dataset group.
*)name : name option;The name of the dataset group.
*)}A dataset group is a collection of related datasets (Item interactions, Users, Items, Actions, Action interactions). You create a dataset group by calling CreateDatasetGroup. You then create a dataset and add it to a dataset group by calling CreateDataset. The dataset group is used to create and train a solution by calling CreateSolution. A dataset group can contain only one of each type of dataset.
You can specify an Key Management Service (KMS) key to encrypt the datasets in the group.
type nonrec describe_dataset_group_response = {dataset_group : dataset_group option;A listing of the dataset group's properties.
*)}type nonrec describe_dataset_group_request = {dataset_group_arn : arn;The Amazon Resource Name (ARN) of the dataset group to describe.
*)}The output configuration parameters of a dataset export job.
type nonrec dataset_export_job = {failure_reason : failure_reason option;If a dataset export job fails, provides the reason why.
*)last_updated_date_time : date option;The date and time (in Unix time) the status of the dataset export job was last updated.
*)creation_date_time : date option;The creation date and time (in Unix time) of the dataset export job.
*)job_output : dataset_export_job_output option;The path to the Amazon S3 bucket where the job's output is stored. For example:
s3://bucket-name/folder-name/
status : status option;The status of the dataset export job.
A dataset export job can be in one of the following states:
role_arn : arn option;The Amazon Resource Name (ARN) of the IAM service role that has permissions to add data to your output Amazon S3 bucket.
*)ingestion_mode : ingestion_mode option;The data to export, based on how you imported the data. You can choose to export BULK data that you imported using a dataset import job, PUT data that you imported incrementally (using the console, PutEvents, PutUsers and PutItems operations), or ALL for both types. The default value is PUT.
dataset_arn : arn option;The Amazon Resource Name (ARN) of the dataset to export.
*)dataset_export_job_arn : arn option;The Amazon Resource Name (ARN) of the dataset export job.
*)job_name : name option;The name of the export job.
*)}Describes a job that exports a dataset to an Amazon S3 bucket. For more information, see CreateDatasetExportJob.
A dataset export job can be in one of the following states:
type nonrec describe_dataset_export_job_response = {dataset_export_job : dataset_export_job option;Information about the dataset export job, including the status.
The status is one of the following values:
}type nonrec describe_dataset_export_job_request = {dataset_export_job_arn : arn;The Amazon Resource Name (ARN) of the dataset export job to describe.
*)}type nonrec data_deletion_job = {failure_reason : failure_reason option;If a data deletion job fails, provides the reason why.
*)last_updated_date_time : date option;The date and time (in Unix time) the data deletion job was last updated.
*)creation_date_time : date option;The creation date and time (in Unix time) of the data deletion job.
*)num_deleted : integer option;The number of records deleted by a COMPLETED job.
*)status : status option;The status of the data deletion job.
A data deletion job can have one of the following statuses:
role_arn : role_arn option;The Amazon Resource Name (ARN) of the IAM role that has permissions to read from the Amazon S3 data source.
*)data_source : data_source option;dataset_group_arn : arn option;The Amazon Resource Name (ARN) of the dataset group the job deletes records from.
*)data_deletion_job_arn : arn option;The Amazon Resource Name (ARN) of the data deletion job.
*)job_name : name option;The name of the data deletion job.
*)}Describes a job that deletes all references to specific users from an Amazon Personalize dataset group in batches. For information about creating a data deletion job, see Deleting users.
type nonrec describe_data_deletion_job_response = {data_deletion_job : data_deletion_job option;Information about the data deletion job, including the status.
The status is one of the following values:
}type nonrec describe_data_deletion_job_request = {data_deletion_job_arn : arn;The Amazon Resource Name (ARN) of the data deletion job.
*)}type nonrec campaign_update_summary = {last_updated_date_time : date option;The date and time (in Unix time) that the campaign update was last updated.
*)creation_date_time : date option;The date and time (in Unix time) that the campaign update was created.
*)failure_reason : failure_reason option;If a campaign update fails, the reason behind the failure.
*)status : status option;The status of the campaign update.
A campaign update can be in one of the following states:
campaign_config : campaign_config option;min_provisioned_tp_s : transactions_per_second option;Specifies the requested minimum provisioned transactions (recommendations) per second that Amazon Personalize will support.
*)solution_version_arn : arn option;The Amazon Resource Name (ARN) of the deployed solution version.
*)}Provides a summary of the properties of a campaign update. For a complete listing, call the DescribeCampaign API.
type nonrec campaign = {latest_campaign_update : campaign_update_summary option;Provides a summary of the properties of a campaign update. For a complete listing, call the DescribeCampaign API.
The latestCampaignUpdate field is only returned when the campaign has had at least one UpdateCampaign call.
last_updated_date_time : date option;The date and time (in Unix format) that the campaign was last updated.
*)creation_date_time : date option;The date and time (in Unix format) that the campaign was created.
*)failure_reason : failure_reason option;If a campaign fails, the reason behind the failure.
*)status : status option;The status of the campaign.
A campaign can be in one of the following states:
campaign_config : campaign_config option;The configuration details of a campaign.
*)min_provisioned_tp_s : transactions_per_second option;Specifies the requested minimum provisioned transactions (recommendations) per second. A high minProvisionedTPS will increase your bill. We recommend starting with 1 for minProvisionedTPS (the default). Track your usage using Amazon CloudWatch metrics, and increase the minProvisionedTPS as necessary.
solution_version_arn : arn option;The Amazon Resource Name (ARN) of the solution version the campaign uses.
*)campaign_arn : arn option;The Amazon Resource Name (ARN) of the campaign.
*)name : name option;The name of the campaign.
*)}An object that describes the deployment of a solution version. For more information on campaigns, see CreateCampaign.
type nonrec describe_campaign_response = {campaign : campaign option;The latestCampaignUpdate field is only returned when the campaign has had at least one UpdateCampaign call.
The properties of the campaign.
The latestCampaignUpdate field is only returned when the campaign has had at least one UpdateCampaign call.
}type nonrec describe_campaign_request = {campaign_arn : arn;The Amazon Resource Name (ARN) of the campaign.
*)}The input configuration of a batch segment job.
The output configuration parameters of a batch segment job.
type nonrec batch_segment_job = {last_updated_date_time : date option;The time at which the batch segment job last updated.
*)creation_date_time : date option;The time at which the batch segment job was created.
*)status : status option;The status of the batch segment job. The status is one of the following values:
role_arn : role_arn option;The ARN of the Amazon Identity and Access Management (IAM) role that requested the batch segment job.
*)job_output : batch_segment_job_output option;The Amazon S3 bucket that contains the output data generated by the batch segment job.
*)job_input : batch_segment_job_input option;The Amazon S3 path that leads to the input data used to generate the batch segment job.
*)num_results : num_batch_results option;The number of predicted users generated by the batch segment job for each line of input data. The maximum number of users per segment is 5 million.
*)solution_version_arn : arn option;The Amazon Resource Name (ARN) of the solution version used by the batch segment job to generate batch segments.
*)failure_reason : failure_reason option;If the batch segment job failed, the reason for the failure.
*)filter_arn : arn option;The ARN of the filter used on the batch segment job.
*)batch_segment_job_arn : arn option;The Amazon Resource Name (ARN) of the batch segment job.
*)job_name : name option;The name of the batch segment job.
*)}Contains information on a batch segment job.
type nonrec describe_batch_segment_job_response = {batch_segment_job : batch_segment_job option;Information on the specified batch segment job.
*)}type nonrec describe_batch_segment_job_request = {batch_segment_job_arn : arn;The ARN of the batch segment job to describe.
*)}type nonrec batch_inference_job_input = {s3_data_source : s3_data_config;The URI of the Amazon S3 location that contains your input data. The Amazon S3 bucket must be in the same region as the API endpoint you are calling.
*)}The input configuration of a batch inference job.
type nonrec batch_inference_job_output = {s3_data_destination : s3_data_config;Information on the Amazon S3 bucket in which the batch inference job's output is stored.
*)}The output configuration parameters of a batch inference job.
type nonrec batch_inference_job_config = {ranking_influence : ranking_influence option;A map of ranking influence values for POPULARITY and FRESHNESS. For each key, specify a numerical value between 0.0 and 1.0 that determines how much influence that ranking factor has on the final recommendations. A value closer to 1.0 gives more weight to the factor, while a value closer to 0.0 reduces its influence.
*)item_exploration_config : hyper_parameters option;A string to string map specifying the exploration configuration hyperparameters, including explorationWeight and explorationItemAgeCutOff, you want to use to configure the amount of item exploration Amazon Personalize uses when recommending items. See User-Personalization.
}The configuration details of a batch inference job.
type nonrec batch_inference_job = {last_updated_date_time : date option;The time at which the batch inference job was last updated.
*)creation_date_time : date option;The time at which the batch inference job was created.
*)status : status option;The status of the batch inference job. The status is one of the following values:
theme_generation_config : theme_generation_config option;The job's theme generation settings.
*)batch_inference_job_mode : batch_inference_job_mode option;The job's mode.
*)role_arn : role_arn option;The ARN of the Amazon Identity and Access Management (IAM) role that requested the batch inference job.
*)batch_inference_job_config : batch_inference_job_config option;A string to string map of the configuration details of a batch inference job.
*)job_output : batch_inference_job_output option;The Amazon S3 bucket that contains the output data generated by the batch inference job.
*)job_input : batch_inference_job_input option;The Amazon S3 path that leads to the input data used to generate the batch inference job.
*)num_results : num_batch_results option;The number of recommendations generated by the batch inference job. This number includes the error messages generated for failed input records.
*)solution_version_arn : arn option;The Amazon Resource Name (ARN) of the solution version from which the batch inference job was created.
*)failure_reason : failure_reason option;If the batch inference job failed, the reason for the failure.
*)filter_arn : arn option;The ARN of the filter used on the batch inference job.
*)batch_inference_job_arn : arn option;The Amazon Resource Name (ARN) of the batch inference job.
*)job_name : name option;The name of the batch inference job.
*)}Contains information on a batch inference job.
type nonrec describe_batch_inference_job_response = {batch_inference_job : batch_inference_job option;Information on the specified batch inference job.
*)}type nonrec describe_batch_inference_job_request = {batch_inference_job_arn : arn;The ARN of the batch inference job to describe.
*)}type nonrec algorithm_image = {docker_ur_i : docker_ur_i;The URI of the Docker container for the algorithm image.
*)name : name option;The name of the algorithm image.
*)}Describes an algorithm image.
type nonrec default_integer_hyper_parameter_range = {is_tunable : tunable option;Indicates whether the hyperparameter is tunable.
*)max_value : integer_max_value option;The maximum allowable value for the hyperparameter.
*)min_value : integer_min_value option;The minimum allowable value for the hyperparameter.
*)name : parameter_name option;The name of the hyperparameter.
*)}Provides the name and default range of a integer-valued hyperparameter and whether the hyperparameter is tunable. A tunable hyperparameter can have its value determined during hyperparameter optimization (HPO).
type nonrec default_integer_hyper_parameter_ranges =
default_integer_hyper_parameter_range listtype nonrec default_continuous_hyper_parameter_range = {is_tunable : tunable option;Whether the hyperparameter is tunable.
*)max_value : continuous_max_value option;The maximum allowable value for the hyperparameter.
*)min_value : continuous_min_value option;The minimum allowable value for the hyperparameter.
*)name : parameter_name option;The name of the hyperparameter.
*)}Provides the name and default range of a continuous hyperparameter and whether the hyperparameter is tunable. A tunable hyperparameter can have its value determined during hyperparameter optimization (HPO).
type nonrec default_continuous_hyper_parameter_ranges =
default_continuous_hyper_parameter_range listtype nonrec default_categorical_hyper_parameter_range = {is_tunable : tunable option;Whether the hyperparameter is tunable.
*)values : categorical_values option;A list of the categories for the hyperparameter.
*)name : parameter_name option;The name of the hyperparameter.
*)}Provides the name and default range of a categorical hyperparameter and whether the hyperparameter is tunable. A tunable hyperparameter can have its value determined during hyperparameter optimization (HPO).
type nonrec default_categorical_hyper_parameter_ranges =
default_categorical_hyper_parameter_range listtype nonrec default_hyper_parameter_ranges = {categorical_hyper_parameter_ranges : default_categorical_hyper_parameter_ranges
option;The categorical hyperparameters and their default ranges.
*)continuous_hyper_parameter_ranges : default_continuous_hyper_parameter_ranges
option;The continuous hyperparameters and their default ranges.
*)integer_hyper_parameter_ranges : default_integer_hyper_parameter_ranges option;The integer-valued hyperparameters and their default ranges.
*)}Specifies the hyperparameters and their default ranges. Hyperparameters can be categorical, continuous, or integer-valued.
type nonrec algorithm = {last_updated_date_time : date option;The date and time (in Unix time) that the algorithm was last updated.
*)creation_date_time : date option;The date and time (in Unix time) that the algorithm was created.
*)role_arn : arn option;The Amazon Resource Name (ARN) of the role.
*)training_input_mode : training_input_mode option;The training input mode.
*)default_resource_config : resource_config option;Specifies the default maximum number of training jobs and parallel training jobs.
*)default_hyper_parameter_ranges : default_hyper_parameter_ranges option;Specifies the default hyperparameters, their ranges, and whether they are tunable. A tunable hyperparameter can have its value determined during hyperparameter optimization (HPO).
*)default_hyper_parameters : hyper_parameters option;Specifies the default hyperparameters.
*)algorithm_image : algorithm_image option;The URI of the Docker container for the algorithm image.
*)algorithm_arn : arn option;The Amazon Resource Name (ARN) of the algorithm.
*)name : name option;The name of the algorithm.
*)}Describes a custom algorithm.
type nonrec describe_algorithm_response = {algorithm : algorithm option;A listing of the properties of the algorithm.
*)}type nonrec describe_algorithm_request = {algorithm_arn : arn;The Amazon Resource Name (ARN) of the algorithm to describe.
*)}type nonrec delete_schema_request = {schema_arn : arn;The Amazon Resource Name (ARN) of the schema to delete.
*)}type nonrec delete_recommender_request = {recommender_arn : arn;The Amazon Resource Name (ARN) of the recommender to delete.
*)}type nonrec delete_metric_attribution_request = {metric_attribution_arn : arn;The metric attribution's Amazon Resource Name (ARN).
*)}type nonrec delete_event_tracker_request = {event_tracker_arn : arn;The Amazon Resource Name (ARN) of the event tracker to delete.
*)}type nonrec delete_dataset_request = {dataset_arn : arn;The Amazon Resource Name (ARN) of the dataset to delete.
*)}type nonrec delete_dataset_group_request = {dataset_group_arn : arn;The ARN of the dataset group to delete.
*)}type nonrec delete_campaign_request = {campaign_arn : arn;The Amazon Resource Name (ARN) of the campaign to delete.
*)}type nonrec create_solution_version_response = {solution_version_arn : arn option;The ARN of the new solution version.
*)}type nonrec create_solution_version_request = {training_mode : training_mode option;The scope of training to be performed when creating the solution version. The default is FULL. This creates a completely new model based on the entirety of the training data from the datasets in your dataset group.
If you use User-Personalization, you can specify a training mode of UPDATE. This updates the model to consider new items for recommendations. It is not a full retraining. You should still complete a full retraining weekly. If you specify UPDATE, Amazon Personalize will stop automatic updates for the solution version. To resume updates, create a new solution with training mode set to FULL and deploy it in a campaign. For more information about automatic updates, see Automatic updates.
The UPDATE option can only be used when you already have an active solution version created from the input solution using the FULL option and the input solution was trained with the User-Personalization recipe or the legacy HRNN-Coldstart recipe.
solution_arn : arn;The Amazon Resource Name (ARN) of the solution containing the training configuration information.
*)name : name option;The name of the solution version.
*)}type nonrec create_solution_request = {solution_config : solution_config option;The configuration properties for the solution. When performAutoML is set to true, Amazon Personalize only evaluates the autoMLConfig section of the solution configuration.
Amazon Personalize doesn't support configuring the hpoObjective at this time.
event_type : event_type option;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.
If you do not provide an eventType, Amazon Personalize will use all interactions for training with equal weight regardless of type.
dataset_group_arn : arn;The Amazon Resource Name (ARN) of the dataset group that provides the training data.
*)recipe_arn : arn option;The Amazon Resource Name (ARN) of the recipe to use for model training. This is required when performAutoML is false. For information about different Amazon Personalize recipes and their ARNs, see Choosing a recipe.
perform_incremental_update : perform_incremental_update option;Whether to perform incremental training updates on your 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.
*)perform_auto_training : perform_auto_training option;Whether the solution uses automatic training to create new solution versions (trained models). The default is True and the solution automatically creates new solution versions every 7 days. You can change the training frequency by specifying a schedulingExpression in the AutoTrainingConfig as part of solution configuration. For more information about automatic training, see Configuring automatic training.
Automatic solution version creation starts within one hour after the solution is ACTIVE. If you manually create a solution version within the hour, the solution skips the first automatic training.
After training starts, you can get the solution version's Amazon Resource Name (ARN) with the ListSolutionVersions API operation. To get its status, use the DescribeSolutionVersion.
*)perform_auto_m_l : perform_auto_m_l option;We don't recommend enabling automated machine learning. Instead, match your use case to the available Amazon Personalize recipes. For more information, see Choosing a recipe.
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.
perform_hp_o : boolean_ option;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.
name : name;The name for the solution.
*)}type nonrec create_schema_response = {schema_arn : arn option;The Amazon Resource Name (ARN) of the created schema.
*)}type nonrec create_schema_request = {domain : domain option;The domain for the schema. If you are creating a schema for a dataset in a Domain dataset group, specify the domain you chose when you created the Domain dataset group.
*)schema : avro_schema;A schema in Avro JSON format.
*)name : name;The name for the schema.
*)}type nonrec create_recommender_response = {recommender_arn : arn option;The Amazon Resource Name (ARN) of the recommender.
*)}type nonrec create_recommender_request = {recommender_config : recommender_config option;The configuration details of the recommender.
*)recipe_arn : arn;The Amazon Resource Name (ARN) of the recipe that the recommender will use. For a recommender, a recipe is a Domain dataset group use case. Only Domain dataset group use cases can be used to create a recommender. For information about use cases see Choosing recommender use cases.
*)dataset_group_arn : arn;The Amazon Resource Name (ARN) of the destination domain dataset group for the recommender.
*)name : name;The name of the recommender.
*)}type nonrec create_metric_attribution_response = {metric_attribution_arn : arn option;The Amazon Resource Name (ARN) for the new metric attribution.
*)}type nonrec create_metric_attribution_request = {metrics_output_config : metric_attribution_output;The output configuration details for the metric attribution.
*)metrics : metric_attributes;A list of metric attributes for the metric attribution. Each metric attribute specifies an event type to track and a function. Available functions are SUM() or SAMPLECOUNT(). For SUM() functions, provide the dataset type (either Interactions or Items) and column to sum as a parameter. For example SUM(Items.PRICE).
dataset_group_arn : arn;The Amazon Resource Name (ARN) of the destination dataset group for the metric attribution.
*)name : name;A name for the metric attribution.
*)}type nonrec create_filter_request = {filter_expression : filter_expression;The filter expression defines which items are included or excluded from recommendations. Filter expression must follow specific format rules. For information about filter expression structure and syntax, see Filter expressions.
*)dataset_group_arn : arn;The ARN of the dataset group that the filter will belong to.
*)name : name;The name of the filter to create.
*)}type nonrec create_event_tracker_response = {tracking_id : tracking_id option;event_tracker_arn : arn option;The ARN of the event tracker.
*)}type nonrec create_dataset_request = {dataset_type : dataset_type;The type of dataset.
One of the following (case insensitive) values:
dataset_group_arn : arn;The Amazon Resource Name (ARN) of the dataset group to add the dataset to.
*)schema_arn : arn;The ARN of the schema to associate with the dataset. The schema defines the dataset fields.
*)name : name;The name for the dataset.
*)}type nonrec create_dataset_import_job_response = {dataset_import_job_arn : arn option;The ARN of the dataset import job.
*)}type nonrec create_dataset_import_job_request = {publish_attribution_metrics_to_s3 : boolean_ option;If you created a metric attribution, specify whether to publish metrics for this import job to Amazon S3
*)import_mode : import_mode option;Specify how to add the new records to an existing dataset. The default import mode is FULL. If you haven't imported bulk records into the dataset previously, you can only specify FULL.
FULL to overwrite all existing bulk data in your dataset. Data you imported individually is not replaced.INCREMENTAL to append the new records to the existing data in your dataset. Amazon Personalize replaces any record with the same ID with the new one.role_arn : role_arn option;The ARN of the IAM role that has permissions to read from the Amazon S3 data source.
*)data_source : data_source;The Amazon S3 bucket that contains the training data to import.
*)dataset_arn : arn;The ARN of the dataset that receives the imported data.
*)job_name : name;The name for the dataset import job.
*)}type nonrec create_dataset_group_request = {domain : domain option;The domain of the dataset group. Specify a domain to create a Domain dataset group. The domain you specify determines the default schemas for datasets and the use cases available for recommenders. If you don't specify a domain, you create a Custom dataset group with solution versions that you deploy with a campaign.
*)kms_key_arn : kms_key_arn option;The Amazon Resource Name (ARN) of a Key Management Service (KMS) key used to encrypt the datasets.
*)role_arn : role_arn option;The ARN of the Identity and Access Management (IAM) role that has permissions to access the Key Management Service (KMS) key. Supplying an IAM role is only valid when also specifying a KMS key.
*)name : name;The name for the new dataset group.
*)}type nonrec create_dataset_export_job_response = {dataset_export_job_arn : arn option;The Amazon Resource Name (ARN) of the dataset export job.
*)}type nonrec create_dataset_export_job_request = {job_output : dataset_export_job_output;The path to the Amazon S3 bucket where the job's output is stored.
*)role_arn : role_arn;The Amazon Resource Name (ARN) of the IAM service role that has permissions to add data to your output Amazon S3 bucket.
*)ingestion_mode : ingestion_mode option;The data to export, based on how you imported the data. You can choose to export only BULK data that you imported using a dataset import job, only PUT data that you imported incrementally (using the console, PutEvents, PutUsers and PutItems operations), or ALL for both types. The default value is PUT.
dataset_arn : arn;The Amazon Resource Name (ARN) of the dataset that contains the data to export.
*)job_name : name;The name for the dataset export job.
*)}type nonrec create_data_deletion_job_response = {data_deletion_job_arn : arn option;The Amazon Resource Name (ARN) of the data deletion job.
*)}type nonrec create_data_deletion_job_request = {role_arn : role_arn;The Amazon Resource Name (ARN) of the IAM role that has permissions to read from the Amazon S3 data source.
*)data_source : data_source;The Amazon S3 bucket that contains the list of userIds of the users to delete.
*)dataset_group_arn : arn;The Amazon Resource Name (ARN) of the dataset group that has the datasets you want to delete records from.
*)job_name : name;The name for the data deletion job.
*)}type nonrec create_campaign_response = {campaign_arn : arn option;The Amazon Resource Name (ARN) of the campaign.
*)}type nonrec create_campaign_request = {campaign_config : campaign_config option;The configuration details of a campaign.
*)min_provisioned_tp_s : transactions_per_second option;Specifies the requested minimum provisioned transactions (recommendations) per second that Amazon Personalize will support. A high minProvisionedTPS will increase your bill. We recommend starting with 1 for minProvisionedTPS (the default). Track your usage using Amazon CloudWatch metrics, and increase the minProvisionedTPS as necessary.
solution_version_arn : arn;The Amazon Resource Name (ARN) of the trained model to deploy with the campaign. To specify the latest solution version of your solution, specify the ARN of your solution in SolutionArn/$LATEST format. You must use this format if you set syncWithLatestSolutionVersion to True in the CampaignConfig.
To deploy a model that isn't the latest solution version of your solution, specify the ARN of the solution version.
For more information about automatic campaign updates, see Enabling automatic campaign updates.
*)name : name;A name for the new campaign. The campaign name must be unique within your account.
*)}type nonrec create_batch_segment_job_response = {batch_segment_job_arn : arn option;The ARN of the batch segment job.
*)}type nonrec create_batch_segment_job_request = {role_arn : role_arn;The ARN of the Amazon Identity and Access Management role that has permissions to read and write to your input and output Amazon S3 buckets respectively.
*)job_output : batch_segment_job_output;The Amazon S3 path for the bucket where the job's output will be stored.
*)job_input : batch_segment_job_input;The Amazon S3 path for the input data used to generate the batch segment job.
*)num_results : num_batch_results option;The number of predicted users generated by the batch segment job for each line of input data. The maximum number of users per segment is 5 million.
*)filter_arn : arn option;The ARN of the filter to apply to the batch segment job. For more information on using filters, see Filtering batch recommendations.
*)solution_version_arn : arn;The Amazon Resource Name (ARN) of the solution version you want the batch segment job to use to generate batch segments.
*)job_name : name;The name of the batch segment job to create.
*)}type nonrec create_batch_inference_job_response = {batch_inference_job_arn : arn option;The ARN of the batch inference job.
*)}type nonrec create_batch_inference_job_request = {theme_generation_config : theme_generation_config option;For theme generation jobs, specify the name of the column in your Items dataset that contains each item's name.
*)batch_inference_job_mode : batch_inference_job_mode option;The mode of the batch inference job. To generate descriptive themes for groups of similar items, set the job mode to THEME_GENERATION. If you don't want to generate themes, use the default BATCH_INFERENCE.
When you get batch recommendations with themes, you will incur additional costs. For more information, see Amazon Personalize pricing.
*)batch_inference_job_config : batch_inference_job_config option;The configuration details of a batch inference job.
*)role_arn : role_arn;The ARN of the Amazon Identity and Access Management role that has permissions to read and write to your input and output Amazon S3 buckets respectively.
*)job_output : batch_inference_job_output;The path to the Amazon S3 bucket where the job's output will be stored.
*)job_input : batch_inference_job_input;The Amazon S3 path that leads to the input file to base your recommendations on. The input material must be in JSON format.
*)num_results : num_batch_results option;The number of recommendations to retrieve.
*)filter_arn : arn option;The ARN of the filter to apply to the batch inference job. For more information on using filters, see Filtering batch recommendations.
*)solution_version_arn : arn;The Amazon Resource Name (ARN) of the solution version that will be used to generate the batch inference recommendations.
*)job_name : name;The name of the batch inference job to create.
*)}