Personalize

create_batch_inference_job(job_input, job_name, job_output, role_arn, solution_version_arn)
create_batch_inference_job(job_input, job_name, job_output, role_arn, solution_version_arn, params::Dict{String,<:Any})

Generates batch recommendations based on a list of items or users stored in Amazon S3 and exports the recommendations to an Amazon S3 bucket. To generate batch recommendations, specify the ARN of a solution version and an Amazon S3 URI for the input and output data. For user personalization, popular items, and personalized ranking solutions, the batch inference job generates a list of recommended items for each user ID in the input file. For related items solutions, the job generates a list of recommended items for each item ID in the input file. For more information, see Creating a batch inference job . If you use the Similar-Items recipe, Amazon Personalize can add descriptive themes to batch recommendations. To generate themes, set the job's mode to THEME_GENERATION and specify the name of the field that contains item names in the input data. For more information about generating themes, see Batch recommendations with themes from Content Generator . You can't get batch recommendations with the Trending-Now or Next-Best-Action recipes.

Arguments

• 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.
• job_name: The name of the batch inference job to create.
• job_output: The path to the Amazon S3 bucket where the job's output will be stored.
• 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.
• solution_version_arn: The Amazon Resource Name (ARN) of the solution version that will be used to generate the batch inference recommendations.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

• "batchInferenceJobConfig": The configuration details of a batch inference job.
• "batchInferenceJobMode": The mode of the batch inference job. To generate descriptive themes for groups of similar items, set the job mode to THEMEGENERATION. If you don't want to generate themes, use the default BATCHINFERENCE. When you get batch recommendations with themes, you will incur additional costs. For more information, see Amazon Personalize pricing.
• "filterArn": The ARN of the filter to apply to the batch inference job. For more information on using filters, see Filtering batch recommendations.
• "numResults": The number of recommendations to retrieve.
• "tags": A list of tags to apply to the batch inference job.
• "themeGenerationConfig": For theme generation jobs, specify the name of the column in your Items dataset that contains each item's name.

create_batch_segment_job(job_input, job_name, job_output, role_arn, solution_version_arn)
create_batch_segment_job(job_input, job_name, job_output, role_arn, solution_version_arn, params::Dict{String,<:Any})

Creates a batch segment job. The operation can handle up to 50 million records and the input file must be in JSON format. For more information, see Getting batch recommendations and user segments.

Arguments

• job_input: The Amazon S3 path for the input data used to generate the batch segment job.
• job_name: The name of the batch segment job to create.
• job_output: The Amazon S3 path for the bucket where the job's output will be stored.
• 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.
• solution_version_arn: The Amazon Resource Name (ARN) of the solution version you want the batch segment job to use to generate batch segments.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

• "filterArn": The ARN of the filter to apply to the batch segment job. For more information on using filters, see Filtering batch recommendations.
• "numResults": 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.
• "tags": A list of tags to apply to the batch segment job.

create_campaign(name, solution_version_arn)
create_campaign(name, solution_version_arn, params::Dict{String,<:Any})

You incur campaign costs while it is active. To avoid unnecessary costs, make sure to delete the campaign when you are finished. For information about campaign costs, see Amazon Personalize pricing. Creates a campaign that deploys a solution version. When a client calls the GetRecommendations and GetPersonalizedRanking APIs, a campaign is specified in the request. Minimum Provisioned TPS and Auto-Scaling A high minProvisionedTPS will increase your cost. We recommend starting with 1 for minProvisionedTPS (the default). Track your usage using Amazon CloudWatch metrics, and increase the minProvisionedTPS as necessary. When you create an Amazon Personalize campaign, you can specify the minimum provisioned transactions per second (minProvisionedTPS) for the campaign. This is the baseline transaction throughput for the campaign provisioned by Amazon Personalize. It sets the minimum billing charge for the campaign while it is active. A transaction is a single GetRecommendations or GetPersonalizedRanking request. The default minProvisionedTPS is 1. If your TPS increases beyond the minProvisionedTPS, Amazon Personalize auto-scales the provisioned capacity up and down, but never below minProvisionedTPS. There's a short time delay while the capacity is increased that might cause loss of transactions. When your traffic reduces, capacity returns to the minProvisionedTPS. You are charged for the the minimum provisioned TPS or, if your requests exceed the minProvisionedTPS, the actual TPS. The actual TPS is the total number of recommendation requests you make. We recommend starting with a low minProvisionedTPS, track your usage using Amazon CloudWatch metrics, and then increase the minProvisionedTPS as necessary. For more information about campaign costs, see Amazon Personalize pricing. Status A campaign can be in one of the following states: CREATE PENDING > CREATE INPROGRESS > ACTIVE -or- CREATE FAILED DELETE PENDING > DELETE INPROGRESS To get the campaign status, call DescribeCampaign. Wait until the status of the campaign is ACTIVE before asking the campaign for recommendations. Related APIs ListCampaigns DescribeCampaign UpdateCampaign DeleteCampaign

Arguments

• name: A name for the new campaign. The campaign name must be unique within your account.
• solution_version_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.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

• "campaignConfig": The configuration details of a campaign.
• "minProvisionedTPS": 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.
• "tags": A list of tags to apply to the campaign.

create_data_deletion_job(data_source, dataset_group_arn, job_name, role_arn)
create_data_deletion_job(data_source, dataset_group_arn, job_name, role_arn, params::Dict{String,<:Any})

Creates a batch job that deletes all references to specific users from an Amazon Personalize dataset group in batches. You specify the users to delete in a CSV file of userIds in an Amazon S3 bucket. After a job completes, Amazon Personalize no longer trains on the users’ data and no longer considers the users when generating user segments. For more information about creating a data deletion job, see Deleting users. Your input file must be a CSV file with a single USERID column that lists the users IDs. For more information about preparing the CSV file, see Preparing your data deletion file and uploading it to Amazon S3. To give Amazon Personalize permission to access your input CSV file of userIds, you must specify an IAM service role that has permission to read from the data source. This role needs GetObject and ListBucket permissions for the bucket and its content. These permissions are the same as importing data. For information on granting access to your Amazon S3 bucket, see Giving Amazon Personalize Access to Amazon S3 Resources. After you create a job, it can take up to a day to delete all references to the users from datasets and models. Until the job completes, Amazon Personalize continues to use the data when training. And if you use a User Segmentation recipe, the users might appear in user segments. Status A data deletion job can have one of the following statuses: PENDING > INPROGRESS > COMPLETED -or- FAILED To get the status of the data deletion job, call DescribeDataDeletionJob API operation and specify the Amazon Resource Name (ARN) of the job. If the status is FAILED, the response includes a failureReason key, which describes why the job failed. Related APIs ListDataDeletionJobs DescribeDataDeletionJob

Arguments

• data_source: The Amazon S3 bucket that contains the list of userIds of the users to delete.
• dataset_group_arn: The Amazon Resource Name (ARN) of the dataset group that has the datasets you want to delete records from.
• job_name: The name for the data deletion job.
• role_arn: The Amazon Resource Name (ARN) of the IAM role that has permissions to read from the Amazon S3 data source.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

• "tags": A list of tags to apply to the data deletion job.

create_dataset(dataset_group_arn, dataset_type, name, schema_arn)
create_dataset(dataset_group_arn, dataset_type, name, schema_arn, params::Dict{String,<:Any})

Creates an empty dataset and adds it to the specified dataset group. Use CreateDatasetImportJob to import your training data to a dataset. There are 5 types of datasets: Item interactions Items Users Action interactions Actions Each dataset type has an associated schema with required field types. Only the Item interactions dataset is required in order to train a model (also referred to as creating a solution). A dataset can be in one of the following states: CREATE PENDING > CREATE INPROGRESS > ACTIVE -or- CREATE FAILED DELETE PENDING > DELETE INPROGRESS To get the status of the dataset, call DescribeDataset. Related APIs CreateDatasetGroup ListDatasets DescribeDataset DeleteDataset

Arguments

• dataset_group_arn: The Amazon Resource Name (ARN) of the dataset group to add the dataset to.
• dataset_type: The type of dataset. One of the following (case insensitive) values: Interactions Items Users Actions Action_Interactions
• name: The name for the dataset.
• schema_arn: The ARN of the schema to associate with the dataset. The schema defines the dataset fields.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

• "tags": A list of tags to apply to the dataset.

create_dataset_export_job(dataset_arn, job_name, job_output, role_arn)
create_dataset_export_job(dataset_arn, job_name, job_output, role_arn, params::Dict{String,<:Any})

Creates a job that exports data from your dataset to an Amazon S3 bucket. To allow Amazon Personalize to export the training data, you must specify an service-linked IAM role that gives Amazon Personalize PutObject permissions for your Amazon S3 bucket. For information, see Exporting a dataset in the Amazon Personalize developer guide. Status A dataset export job can be in one of the following states: CREATE PENDING > CREATE IN_PROGRESS > ACTIVE -or- CREATE FAILED To get the status of the export job, call DescribeDatasetExportJob, and specify the Amazon Resource Name (ARN) of the dataset export job. The dataset export is complete when the status shows as ACTIVE. If the status shows as CREATE FAILED, the response includes a failureReason key, which describes why the job failed.

Arguments

• dataset_arn: The Amazon Resource Name (ARN) of the dataset that contains the data to export.
• job_name: The name for the dataset export job.
• job_output: The path to the Amazon S3 bucket where the job's output is stored.
• role_arn: The Amazon Resource Name (ARN) of the IAM service role that has permissions to add data to your output Amazon S3 bucket.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

• "ingestionMode": 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.
• "tags": A list of tags to apply to the dataset export job.

create_dataset_group(name)
create_dataset_group(name, params::Dict{String,<:Any})

Creates an empty dataset group. A dataset group is a container for Amazon Personalize resources. A dataset group can contain at most three datasets, one for each type of dataset: Item interactions Items Users Actions Action interactions A dataset group can be a Domain dataset group, where you specify a domain and use pre-configured resources like recommenders, or a Custom dataset group, where you use custom resources, such as a solution with a solution version, that you deploy with a campaign. If you start with a Domain dataset group, you can still add custom resources such as solutions and solution versions trained with recipes for custom use cases and deployed with campaigns. A dataset group can be in one of the following states: CREATE PENDING > CREATE IN_PROGRESS > ACTIVE -or- CREATE FAILED DELETE PENDING To get the status of the dataset group, call DescribeDatasetGroup. If the status shows as CREATE FAILED, the response includes a failureReason key, which describes why the creation failed. You must wait until the status of the dataset group is ACTIVE before adding a dataset to the group. You can specify an Key Management Service (KMS) key to encrypt the datasets in the group. If you specify a KMS key, you must also include an Identity and Access Management (IAM) role that has permission to access the key. APIs that require a dataset group ARN in the request CreateDataset CreateEventTracker CreateSolution Related APIs ListDatasetGroups DescribeDatasetGroup DeleteDatasetGroup

Arguments

• name: The name for the new dataset group.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

• "domain": 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.
• "kmsKeyArn": The Amazon Resource Name (ARN) of a Key Management Service (KMS) key used to encrypt the datasets.
• "roleArn": 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.
• "tags": A list of tags to apply to the dataset group.

create_dataset_import_job(data_source, dataset_arn, job_name, role_arn)
create_dataset_import_job(data_source, dataset_arn, job_name, role_arn, params::Dict{String,<:Any})

Creates a job that imports training data from your data source (an Amazon S3 bucket) to an Amazon Personalize dataset. To allow Amazon Personalize to import the training data, you must specify an IAM service role that has permission to read from the data source, as Amazon Personalize makes a copy of your data and processes it internally. For information on granting access to your Amazon S3 bucket, see Giving Amazon Personalize Access to Amazon S3 Resources. If you already created a recommender or deployed a custom solution version with a campaign, how new bulk records influence recommendations depends on the domain use case or recipe that you use. For more information, see How new data influences real-time recommendations. By default, a dataset import job replaces any existing data in the dataset that you imported in bulk. To add new records without replacing existing data, specify INCREMENTAL for the import mode in the CreateDatasetImportJob operation. Status A dataset import job can be in one of the following states: CREATE PENDING > CREATE IN_PROGRESS > ACTIVE -or- CREATE FAILED To get the status of the import job, call DescribeDatasetImportJob, providing the Amazon Resource Name (ARN) of the dataset import job. The dataset import is complete when the status shows as ACTIVE. If the status shows as CREATE FAILED, the response includes a failureReason key, which describes why the job failed. Importing takes time. You must wait until the status shows as ACTIVE before training a model using the dataset. Related APIs ListDatasetImportJobs DescribeDatasetImportJob

Arguments

• data_source: The Amazon S3 bucket that contains the training data to import.
• dataset_arn: The ARN of the dataset that receives the imported data.
• job_name: The name for the dataset import job.
• role_arn: The ARN of the IAM role that has permissions to read from the Amazon S3 data source.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

• "importMode": 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. Specify FULL to overwrite all existing bulk data in your dataset. Data you imported individually is not replaced. Specify 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.
• "publishAttributionMetricsToS3": If you created a metric attribution, specify whether to publish metrics for this import job to Amazon S3
• "tags": A list of tags to apply to the dataset import job.

create_event_tracker(dataset_group_arn, name)
create_event_tracker(dataset_group_arn, name, params::Dict{String,<:Any})

Creates an event tracker that you use when adding event data to a specified dataset group using the PutEvents API. Only one event tracker can be associated with a dataset group. You will get an error if you call CreateEventTracker using the same dataset group as an existing event tracker. When you create an event tracker, the response includes a tracking ID, which you pass as a parameter when you use the PutEvents operation. Amazon Personalize then appends the event data to the Item interactions dataset of the dataset group you specify in your event tracker. The event tracker can be in one of the following states: CREATE PENDING > CREATE INPROGRESS > ACTIVE -or- CREATE FAILED DELETE PENDING > DELETE INPROGRESS To get the status of the event tracker, call DescribeEventTracker. The event tracker must be in the ACTIVE state before using the tracking ID. Related APIs ListEventTrackers DescribeEventTracker DeleteEventTracker

Arguments

• dataset_group_arn: The Amazon Resource Name (ARN) of the dataset group that receives the event data.
• name: The name for the event tracker.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

• "tags": A list of tags to apply to the event tracker.

create_filter(dataset_group_arn, filter_expression, name)
create_filter(dataset_group_arn, filter_expression, name, params::Dict{String,<:Any})

Creates a recommendation filter. For more information, see Filtering recommendations and user segments.

Arguments

• dataset_group_arn: The ARN of the dataset group that the filter will belong to.
• 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.
• name: The name of the filter to create.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

• "tags": A list of tags to apply to the filter.

create_metric_attribution(dataset_group_arn, metrics, metrics_output_config, name)
create_metric_attribution(dataset_group_arn, metrics, metrics_output_config, name, params::Dict{String,<:Any})

Creates a metric attribution. A metric attribution creates reports on the data that you import into Amazon Personalize. Depending on how you imported the data, you can view reports in Amazon CloudWatch or Amazon S3. For more information, see Measuring impact of recommendations.

Arguments

• dataset_group_arn: The Amazon Resource Name (ARN) of the destination dataset group for the metric attribution.
• metrics: 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).
• metrics_output_config: The output configuration details for the metric attribution.
• name: A name for the metric attribution.

create_recommender(dataset_group_arn, name, recipe_arn)
create_recommender(dataset_group_arn, name, recipe_arn, params::Dict{String,<:Any})

Creates a recommender with the recipe (a Domain dataset group use case) you specify. You create recommenders for a Domain dataset group and specify the recommender's Amazon Resource Name (ARN) when you make a GetRecommendations request. Minimum recommendation requests per second 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. When you create a recommender, you can configure the recommender's minimum recommendation requests per second. The minimum recommendation requests per second (minRecommendationRequestsPerSecond) specifies the baseline recommendation request throughput provisioned by Amazon Personalize. The default minRecommendationRequestsPerSecond is 1. A recommendation request is a single GetRecommendations operation. Request throughput is measured in requests per second and Amazon Personalize uses your requests per second to derive your requests per hour and the price of your recommender usage. If your requests per second increases beyond minRecommendationRequestsPerSecond, Amazon Personalize auto-scales the provisioned capacity up and down, but never below minRecommendationRequestsPerSecond. There's a short time delay while the capacity is increased that might cause loss of requests. Your bill is the greater of either the minimum requests per hour (based on minRecommendationRequestsPerSecond) or the actual number of requests. The actual request throughput used is calculated as the average requests/second within a one-hour window. We recommend starting with the default minRecommendationRequestsPerSecond, track your usage using Amazon CloudWatch metrics, and then increase the minRecommendationRequestsPerSecond as necessary. Status A recommender can be in one of the following states: CREATE PENDING > CREATE INPROGRESS > ACTIVE -or- CREATE FAILED STOP PENDING > STOP INPROGRESS > INACTIVE > START PENDING > START INPROGRESS > ACTIVE DELETE PENDING > DELETE INPROGRESS To get the recommender status, call DescribeRecommender. Wait until the status of the recommender is ACTIVE before asking the recommender for recommendations. Related APIs ListRecommenders DescribeRecommender UpdateRecommender DeleteRecommender

Arguments

• dataset_group_arn: The Amazon Resource Name (ARN) of the destination domain dataset group for the recommender.
• name: The name of the recommender.
• recipe_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.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

• "recommenderConfig": The configuration details of the recommender.
• "tags": A list of tags to apply to the recommender.

create_schema(name, schema)
create_schema(name, schema, params::Dict{String,<:Any})

Creates an Amazon Personalize schema from the specified schema string. The schema you create must be in Avro JSON format. Amazon Personalize recognizes three schema variants. Each schema is associated with a dataset type and has a set of required field and keywords. If you are creating a schema for a dataset in a Domain dataset group, you provide the domain of the Domain dataset group. You specify a schema when you call CreateDataset. Related APIs ListSchemas DescribeSchema DeleteSchema

Arguments

• name: The name for the schema.
• schema: A schema in Avro JSON format.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

• "domain": 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.

create_solution(dataset_group_arn, name)
create_solution(dataset_group_arn, name, params::Dict{String,<:Any})

After you create a solution, you can’t change its configuration. By default, all new solutions use automatic training. With automatic training, you incur training costs while your solution is active. You can't stop automatic training for a solution. To avoid unnecessary costs, make sure to delete the solution when you are finished. For information about training costs, see Amazon Personalize pricing. Creates the configuration for training a model (creating a solution version). This configuration includes the recipe to use for model training and optional training configuration, such as columns to use in training and feature transformation parameters. For more information about configuring a solution, see Creating and configuring a solution. By default, new solutions use automatic training to create solution versions every 7 days. You can change the training frequency. Automatic solution version creation starts one hour after the solution is ACTIVE. If you manually create a solution version within the hour, the solution skips the first automatic training. For more information, see Configuring automatic training. To turn off automatic training, set performAutoTraining to false. If you turn off automatic training, you must manually create a solution version by calling the CreateSolutionVersion operation. 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. After training completes you can evaluate model accuracy by calling GetSolutionMetrics. When you are satisfied with the solution version, you deploy it using CreateCampaign. The campaign provides recommendations to a client through the GetRecommendations API. Amazon Personalize doesn't support configuring the hpoObjective for solution hyperparameter optimization at this time. Status A solution can be in one of the following states: CREATE PENDING > CREATE INPROGRESS > ACTIVE -or- CREATE FAILED DELETE PENDING > DELETE INPROGRESS To get the status of the solution, call DescribeSolution. If you use manual training, the status must be ACTIVE before you call CreateSolutionVersion. Related APIs ListSolutions CreateSolutionVersion DescribeSolution DeleteSolution ListSolutionVersions DescribeSolutionVersion

Arguments

• dataset_group_arn: The Amazon Resource Name (ARN) of the dataset group that provides the training data.
• name: The name for the solution.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

• "eventType": When your have multiple event types (using an EVENT_TYPE schema field), this parameter specifies which event type (for example, 'click' or 'like') is used for training the model. If you do not provide an eventType, Amazon Personalize will use all interactions for training with equal weight regardless of type.
• "performAutoML": 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.
• "performAutoTraining": 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 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.
• "performHPO": Whether to perform hyperparameter optimization (HPO) on the specified or selected recipe. The default is false. When performing AutoML, this parameter is always true and you should not set it to false.
• "recipeArn": 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.
• "solutionConfig": The configuration to use with the solution. When performAutoML is set to true, Amazon Personalize only evaluates the autoMLConfig section of the solution configuration. Amazon Personalize doesn't support configuring the hpoObjective at this time.
• "tags": A list of tags to apply to the solution.

create_solution_version(solution_arn)
create_solution_version(solution_arn, params::Dict{String,<:Any})

Trains or retrains an active solution in a Custom dataset group. A solution is created using the CreateSolution operation and must be in the ACTIVE state before calling CreateSolutionVersion. A new version of the solution is created every time you call this operation. Status A solution version can be in one of the following states: CREATE PENDING CREATE IN_PROGRESS ACTIVE CREATE FAILED CREATE STOPPING CREATE STOPPED To get the status of the version, call DescribeSolutionVersion. Wait until the status shows as ACTIVE before calling CreateCampaign. If the status shows as CREATE FAILED, the response includes a failureReason key, which describes why the job failed. Related APIs ListSolutionVersions DescribeSolutionVersion ListSolutions CreateSolution DescribeSolution DeleteSolution

Arguments

• solution_arn: The Amazon Resource Name (ARN) of the solution containing the training configuration information.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

• "name": The name of the solution version.
• "tags": A list of tags to apply to the solution version.
• "trainingMode": 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.

delete_campaign(campaign_arn)
delete_campaign(campaign_arn, params::Dict{String,<:Any})

Removes a campaign by deleting the solution deployment. The solution that the campaign is based on is not deleted and can be redeployed when needed. A deleted campaign can no longer be specified in a GetRecommendations request. For information on creating campaigns, see CreateCampaign.

Arguments

• campaign_arn: The Amazon Resource Name (ARN) of the campaign to delete.

delete_dataset(dataset_arn)
delete_dataset(dataset_arn, params::Dict{String,<:Any})

Deletes a dataset. You can't delete a dataset if an associated DatasetImportJob or SolutionVersion is in the CREATE PENDING or IN PROGRESS state. For more information on datasets, see CreateDataset.

Arguments

• dataset_arn: The Amazon Resource Name (ARN) of the dataset to delete.

delete_dataset_group(dataset_group_arn)
delete_dataset_group(dataset_group_arn, params::Dict{String,<:Any})

Deletes a dataset group. Before you delete a dataset group, you must delete the following: All associated event trackers. All associated solutions. All datasets in the dataset group.

Arguments

• dataset_group_arn: The ARN of the dataset group to delete.

delete_event_tracker(event_tracker_arn)
delete_event_tracker(event_tracker_arn, params::Dict{String,<:Any})

Deletes the event tracker. Does not delete the dataset from the dataset group. For more information on event trackers, see CreateEventTracker.

Arguments

• event_tracker_arn: The Amazon Resource Name (ARN) of the event tracker to delete.

delete_filter(filter_arn)
delete_filter(filter_arn, params::Dict{String,<:Any})

Deletes a filter.

Arguments

• filter_arn: The ARN of the filter to delete.

delete_metric_attribution(metric_attribution_arn)
delete_metric_attribution(metric_attribution_arn, params::Dict{String,<:Any})

Deletes a metric attribution.

Arguments

• metric_attribution_arn: The metric attribution's Amazon Resource Name (ARN).

delete_recommender(recommender_arn)
delete_recommender(recommender_arn, params::Dict{String,<:Any})

Deactivates and removes a recommender. A deleted recommender can no longer be specified in a GetRecommendations request.

Arguments

• recommender_arn: The Amazon Resource Name (ARN) of the recommender to delete.

delete_schema(schema_arn)
delete_schema(schema_arn, params::Dict{String,<:Any})

Deletes a schema. Before deleting a schema, you must delete all datasets referencing the schema. For more information on schemas, see CreateSchema.

Arguments

• schema_arn: The Amazon Resource Name (ARN) of the schema to delete.

delete_solution(solution_arn)
delete_solution(solution_arn, params::Dict{String,<:Any})

Deletes all versions of a solution and the Solution object itself. Before deleting a solution, you must delete all campaigns based on the solution. To determine what campaigns are using the solution, call ListCampaigns and supply the Amazon Resource Name (ARN) of the solution. You can't delete a solution if an associated SolutionVersion is in the CREATE PENDING or IN PROGRESS state. For more information on solutions, see CreateSolution.

Arguments

• solution_arn: The ARN of the solution to delete.

describe_algorithm(algorithm_arn)
describe_algorithm(algorithm_arn, params::Dict{String,<:Any})

Describes the given algorithm.

Arguments

• algorithm_arn: The Amazon Resource Name (ARN) of the algorithm to describe.

describe_batch_inference_job(batch_inference_job_arn)
describe_batch_inference_job(batch_inference_job_arn, params::Dict{String,<:Any})

Gets the properties of a batch inference job including name, Amazon Resource Name (ARN), status, input and output configurations, and the ARN of the solution version used to generate the recommendations.

Arguments

• batch_inference_job_arn: The ARN of the batch inference job to describe.

describe_batch_segment_job(batch_segment_job_arn)
describe_batch_segment_job(batch_segment_job_arn, params::Dict{String,<:Any})

Gets the properties of a batch segment job including name, Amazon Resource Name (ARN), status, input and output configurations, and the ARN of the solution version used to generate segments.

Arguments

• batch_segment_job_arn: The ARN of the batch segment job to describe.

describe_campaign(campaign_arn)
describe_campaign(campaign_arn, params::Dict{String,<:Any})

Describes the given campaign, including its status. A campaign can be in one of the following states: CREATE PENDING > CREATE INPROGRESS > ACTIVE -or- CREATE FAILED DELETE PENDING > DELETE INPROGRESS When the status is CREATE FAILED, the response includes the failureReason key, which describes why. For more information on campaigns, see CreateCampaign.

Arguments

• campaign_arn: The Amazon Resource Name (ARN) of the campaign.

describe_data_deletion_job(data_deletion_job_arn)
describe_data_deletion_job(data_deletion_job_arn, params::Dict{String,<:Any})

Describes the data deletion job created by CreateDataDeletionJob, including the job status.

Arguments

• data_deletion_job_arn: The Amazon Resource Name (ARN) of the data deletion job.

describe_dataset(dataset_arn)
describe_dataset(dataset_arn, params::Dict{String,<:Any})

Describes the given dataset. For more information on datasets, see CreateDataset.

Arguments

• dataset_arn: The Amazon Resource Name (ARN) of the dataset to describe.

describe_dataset_export_job(dataset_export_job_arn)
describe_dataset_export_job(dataset_export_job_arn, params::Dict{String,<:Any})

Describes the dataset export job created by CreateDatasetExportJob, including the export job status.

Arguments

• dataset_export_job_arn: The Amazon Resource Name (ARN) of the dataset export job to describe.

describe_dataset_group(dataset_group_arn)
describe_dataset_group(dataset_group_arn, params::Dict{String,<:Any})

Describes the given dataset group. For more information on dataset groups, see CreateDatasetGroup.

Arguments

• dataset_group_arn: The Amazon Resource Name (ARN) of the dataset group to describe.

describe_dataset_import_job(dataset_import_job_arn)
describe_dataset_import_job(dataset_import_job_arn, params::Dict{String,<:Any})

Describes the dataset import job created by CreateDatasetImportJob, including the import job status.

Arguments

• dataset_import_job_arn: The Amazon Resource Name (ARN) of the dataset import job to describe.

describe_event_tracker(event_tracker_arn)
describe_event_tracker(event_tracker_arn, params::Dict{String,<:Any})

Describes an event tracker. The response includes the trackingId and status of the event tracker. For more information on event trackers, see CreateEventTracker.

Arguments

• event_tracker_arn: The Amazon Resource Name (ARN) of the event tracker to describe.

describe_feature_transformation(feature_transformation_arn)
describe_feature_transformation(feature_transformation_arn, params::Dict{String,<:Any})

Describes the given feature transformation.

Arguments

• feature_transformation_arn: The Amazon Resource Name (ARN) of the feature transformation to describe.

describe_filter(filter_arn)
describe_filter(filter_arn, params::Dict{String,<:Any})

Describes a filter's properties.

Arguments

• filter_arn: The ARN of the filter to describe.

describe_metric_attribution(metric_attribution_arn)
describe_metric_attribution(metric_attribution_arn, params::Dict{String,<:Any})

Describes a metric attribution.

Arguments

• metric_attribution_arn: The metric attribution's Amazon Resource Name (ARN).

describe_recipe(recipe_arn)
describe_recipe(recipe_arn, params::Dict{String,<:Any})

Describes a recipe. A recipe contains three items: An algorithm that trains a model. Hyperparameters that govern the training. Feature transformation information for modifying the input data before training. Amazon Personalize provides a set of predefined recipes. You specify a recipe when you create a solution with the CreateSolution API. CreateSolution trains a model by using the algorithm in the specified recipe and a training dataset. The solution, when deployed as a campaign, can provide recommendations using the GetRecommendations API.

Arguments

• recipe_arn: The Amazon Resource Name (ARN) of the recipe to describe.

describe_recommender(recommender_arn)
describe_recommender(recommender_arn, params::Dict{String,<:Any})

Describes the given recommender, including its status. A recommender can be in one of the following states: CREATE PENDING > CREATE INPROGRESS > ACTIVE -or- CREATE FAILED STOP PENDING > STOP INPROGRESS > INACTIVE > START PENDING > START INPROGRESS > ACTIVE DELETE PENDING > DELETE INPROGRESS When the status is CREATE FAILED, the response includes the failureReason key, which describes why. The modelMetrics key is null when the recommender is being created or deleted. For more information on recommenders, see CreateRecommender.

Arguments

• recommender_arn: The Amazon Resource Name (ARN) of the recommender to describe.

describe_schema(schema_arn)
describe_schema(schema_arn, params::Dict{String,<:Any})

Describes a schema. For more information on schemas, see CreateSchema.

Arguments

• schema_arn: The Amazon Resource Name (ARN) of the schema to retrieve.

describe_solution(solution_arn)
describe_solution(solution_arn, params::Dict{String,<:Any})

Describes a solution. For more information on solutions, see CreateSolution.

Arguments

• solution_arn: The Amazon Resource Name (ARN) of the solution to describe.

describe_solution_version(solution_version_arn)
describe_solution_version(solution_version_arn, params::Dict{String,<:Any})

Describes a specific version of a solution. For more information on solutions, see CreateSolution

Arguments

• solution_version_arn: The Amazon Resource Name (ARN) of the solution version.

get_solution_metrics(solution_version_arn)
get_solution_metrics(solution_version_arn, params::Dict{String,<:Any})

Gets the metrics for the specified solution version.

Arguments

• solution_version_arn: The Amazon Resource Name (ARN) of the solution version for which to get metrics.

list_batch_inference_jobs()
list_batch_inference_jobs(params::Dict{String,<:Any})

Gets a list of the batch inference jobs that have been performed off of a solution version.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

• "maxResults": The maximum number of batch inference job results to return in each page. The default value is 100.
• "nextToken": The token to request the next page of results.
• "solutionVersionArn": The Amazon Resource Name (ARN) of the solution version from which the batch inference jobs were created.

list_batch_segment_jobs()
list_batch_segment_jobs(params::Dict{String,<:Any})

Gets a list of the batch segment jobs that have been performed off of a solution version that you specify.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

• "maxResults": The maximum number of batch segment job results to return in each page. The default value is 100.
• "nextToken": The token to request the next page of results.
• "solutionVersionArn": The Amazon Resource Name (ARN) of the solution version that the batch segment jobs used to generate batch segments.

list_campaigns()
list_campaigns(params::Dict{String,<:Any})

Returns a list of campaigns that use the given solution. When a solution is not specified, all the campaigns associated with the account are listed. The response provides the properties for each campaign, including the Amazon Resource Name (ARN). For more information on campaigns, see CreateCampaign.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

• "maxResults": The maximum number of campaigns to return.
• "nextToken": A token returned from the previous call to ListCampaigns for getting the next set of campaigns (if they exist).
• "solutionArn": 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.

list_data_deletion_jobs()
list_data_deletion_jobs(params::Dict{String,<:Any})

Returns a list of data deletion jobs for a dataset group ordered by creation time, with the most recent first. When a dataset group is not specified, all the data deletion jobs associated with the account are listed. The response provides the properties for each job, including the Amazon Resource Name (ARN). For more information on data deletion jobs, see Deleting users.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

• "datasetGroupArn": The Amazon Resource Name (ARN) of the dataset group to list data deletion jobs for.
• "maxResults": The maximum number of data deletion jobs to return.
• "nextToken": A token returned from the previous call to ListDataDeletionJobs for getting the next set of jobs (if they exist).

list_dataset_export_jobs()
list_dataset_export_jobs(params::Dict{String,<:Any})

Returns a list of dataset export jobs that use the given dataset. When a dataset is not specified, all the dataset export jobs associated with the account are listed. The response provides the properties for each dataset export job, including the Amazon Resource Name (ARN). For more information on dataset export jobs, see CreateDatasetExportJob. For more information on datasets, see CreateDataset.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

• "datasetArn": The Amazon Resource Name (ARN) of the dataset to list the dataset export jobs for.
• "maxResults": The maximum number of dataset export jobs to return.
• "nextToken": A token returned from the previous call to ListDatasetExportJobs for getting the next set of dataset export jobs (if they exist).

list_dataset_groups()
list_dataset_groups(params::Dict{String,<:Any})

Returns a list of dataset groups. The response provides the properties for each dataset group, including the Amazon Resource Name (ARN). For more information on dataset groups, see CreateDatasetGroup.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

• "maxResults": The maximum number of dataset groups to return.
• "nextToken": A token returned from the previous call to ListDatasetGroups for getting the next set of dataset groups (if they exist).

list_dataset_import_jobs()
list_dataset_import_jobs(params::Dict{String,<:Any})

Returns a list of dataset import jobs that use the given dataset. When a dataset is not specified, all the dataset import jobs associated with the account are listed. The response provides the properties for each dataset import job, including the Amazon Resource Name (ARN). For more information on dataset import jobs, see CreateDatasetImportJob. For more information on datasets, see CreateDataset.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

• "datasetArn": The Amazon Resource Name (ARN) of the dataset to list the dataset import jobs for.
• "maxResults": The maximum number of dataset import jobs to return.
• "nextToken": A token returned from the previous call to ListDatasetImportJobs for getting the next set of dataset import jobs (if they exist).

list_datasets()
list_datasets(params::Dict{String,<:Any})

Returns the list of datasets contained in the given dataset group. The response provides the properties for each dataset, including the Amazon Resource Name (ARN). For more information on datasets, see CreateDataset.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

• "datasetGroupArn": The Amazon Resource Name (ARN) of the dataset group that contains the datasets to list.
• "maxResults": The maximum number of datasets to return.
• "nextToken": A token returned from the previous call to ListDatasets for getting the next set of dataset import jobs (if they exist).

list_event_trackers()
list_event_trackers(params::Dict{String,<:Any})

Returns the list of event trackers associated with the account. The response provides the properties for each event tracker, including the Amazon Resource Name (ARN) and tracking ID. For more information on event trackers, see CreateEventTracker.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

• "datasetGroupArn": The ARN of a dataset group used to filter the response.
• "maxResults": The maximum number of event trackers to return.
• "nextToken": A token returned from the previous call to ListEventTrackers for getting the next set of event trackers (if they exist).

list_filters()
list_filters(params::Dict{String,<:Any})

Lists all filters that belong to a given dataset group.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

• "datasetGroupArn": The ARN of the dataset group that contains the filters.
• "maxResults": The maximum number of filters to return.
• "nextToken": A token returned from the previous call to ListFilters for getting the next set of filters (if they exist).

list_metric_attribution_metrics()
list_metric_attribution_metrics(params::Dict{String,<:Any})

Lists the metrics for the metric attribution.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

• "maxResults": The maximum number of metrics to return in one page of results.
• "metricAttributionArn": The Amazon Resource Name (ARN) of the metric attribution to retrieve attributes for.
• "nextToken": Specify the pagination token from a previous request to retrieve the next page of results.

list_metric_attributions()
list_metric_attributions(params::Dict{String,<:Any})

Lists metric attributions.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

• "datasetGroupArn": The metric attributions' dataset group Amazon Resource Name (ARN).
• "maxResults": The maximum number of metric attributions to return in one page of results.
• "nextToken": Specify the pagination token from a previous request to retrieve the next page of results.

list_recipes()
list_recipes(params::Dict{String,<:Any})

Returns a list of available recipes. The response provides the properties for each recipe, including the recipe's Amazon Resource Name (ARN).

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

• "domain": 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.
• "maxResults": The maximum number of recipes to return.
• "nextToken": A token returned from the previous call to ListRecipes for getting the next set of recipes (if they exist).
• "recipeProvider": The default is SERVICE.

list_recommenders()
list_recommenders(params::Dict{String,<:Any})

Returns a list of recommenders in a given Domain dataset group. When a Domain dataset group is not specified, all the recommenders associated with the account are listed. The response provides the properties for each recommender, including the Amazon Resource Name (ARN). For more information on recommenders, see CreateRecommender.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

• "datasetGroupArn": 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.
• "maxResults": The maximum number of recommenders to return.
• "nextToken": A token returned from the previous call to ListRecommenders for getting the next set of recommenders (if they exist).

list_schemas()
list_schemas(params::Dict{String,<:Any})

Returns the list of schemas associated with the account. The response provides the properties for each schema, including the Amazon Resource Name (ARN). For more information on schemas, see CreateSchema.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

• "maxResults": The maximum number of schemas to return.
• "nextToken": A token returned from the previous call to ListSchemas for getting the next set of schemas (if they exist).

list_solution_versions()
list_solution_versions(params::Dict{String,<:Any})

Returns a list of solution versions for the given solution. When a solution is not specified, all the solution versions associated with the account are listed. The response provides the properties for each solution version, including the Amazon Resource Name (ARN).

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

• "maxResults": The maximum number of solution versions to return.
• "nextToken": A token returned from the previous call to ListSolutionVersions for getting the next set of solution versions (if they exist).
• "solutionArn": The Amazon Resource Name (ARN) of the solution.

list_solutions()
list_solutions(params::Dict{String,<:Any})

Returns a list of solutions in a given dataset group. When a dataset group is not specified, all the solutions associated with the account are listed. The response provides the properties for each solution, including the Amazon Resource Name (ARN). For more information on solutions, see CreateSolution.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

• "datasetGroupArn": The Amazon Resource Name (ARN) of the dataset group.
• "maxResults": The maximum number of solutions to return.
• "nextToken": A token returned from the previous call to ListSolutions for getting the next set of solutions (if they exist).

list_tags_for_resource(resource_arn)
list_tags_for_resource(resource_arn, params::Dict{String,<:Any})

Get a list of tags attached to a resource.

Arguments

• resource_arn: The resource's Amazon Resource Name (ARN).

start_recommender(recommender_arn)
start_recommender(recommender_arn, params::Dict{String,<:Any})

Starts a recommender that is INACTIVE. Starting a recommender does not create any new models, but resumes billing and automatic retraining for the recommender.

Arguments

• recommender_arn: The Amazon Resource Name (ARN) of the recommender to start.

stop_recommender(recommender_arn)
stop_recommender(recommender_arn, params::Dict{String,<:Any})

Stops a recommender that is ACTIVE. Stopping a recommender halts billing and automatic retraining for the recommender.

Arguments

• recommender_arn: The Amazon Resource Name (ARN) of the recommender to stop.

stop_solution_version_creation(solution_version_arn)
stop_solution_version_creation(solution_version_arn, params::Dict{String,<:Any})

Stops creating a solution version that is in a state of CREATEPENDING or CREATE INPROGRESS. Depending on the current state of the solution version, the solution version state changes as follows: CREATEPENDING > CREATESTOPPED or CREATEINPROGRESS > CREATESTOPPING > CREATESTOPPED You are billed for all of the training completed up until you stop the solution version creation. You cannot resume creating a solution version once it has been stopped.

Arguments

• solution_version_arn: The Amazon Resource Name (ARN) of the solution version you want to stop creating.

tag_resource(resource_arn, tags)
tag_resource(resource_arn, tags, params::Dict{String,<:Any})

Add a list of tags to a resource.

Arguments

• resource_arn: The resource's Amazon Resource Name (ARN).
• tags: Tags to apply to the resource. For more information see Tagging Amazon Personalize resources.

untag_resource(resource_arn, tag_keys)
untag_resource(resource_arn, tag_keys, params::Dict{String,<:Any})

Removes the specified tags that are attached to a resource. For more information, see Removing tags from Amazon Personalize resources.

Arguments

• resource_arn: The resource's Amazon Resource Name (ARN).
• tag_keys: The keys of the tags to be removed.

update_campaign(campaign_arn)
update_campaign(campaign_arn, params::Dict{String,<:Any})

Updates a campaign to deploy a retrained solution version with an existing campaign, change your campaign's minProvisionedTPS, or modify your campaign's configuration. For example, you can set enableMetadataWithRecommendations to true for an existing campaign. To update a campaign to start automatically using the latest solution version, specify the following: For the SolutionVersionArn parameter, specify the Amazon Resource Name (ARN) of your solution in SolutionArn/LATEST format. In the campaignConfig, set syncWithLatestSolutionVersion to true. To update a campaign, the campaign status must be ACTIVE or CREATE FAILED. Check the campaign status using the DescribeCampaign operation. You can still get recommendations from a campaign while an update is in progress. The campaign will use the previous solution version and campaign configuration to generate recommendations until the latest campaign update status is Active. For more information about updating a campaign, including code samples, see Updating a campaign. For more information about campaigns, see Creating a campaign.

Arguments

• campaign_arn: The Amazon Resource Name (ARN) of the campaign.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

• "campaignConfig": The configuration details of a campaign.
• "minProvisionedTPS": 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.
• "solutionVersionArn": 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.

update_dataset(dataset_arn, schema_arn)
update_dataset(dataset_arn, schema_arn, params::Dict{String,<:Any})

Update a dataset to replace its schema with a new or existing one. For more information, see Replacing a dataset's schema.

Arguments

• dataset_arn: The Amazon Resource Name (ARN) of the dataset that you want to update.
• schema_arn: The Amazon Resource Name (ARN) of the new schema you want use.

update_metric_attribution()
update_metric_attribution(params::Dict{String,<:Any})

Updates a metric attribution.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

• "addMetrics": Add new metric attributes to the metric attribution.
• "metricAttributionArn": The Amazon Resource Name (ARN) for the metric attribution to update.
• "metricsOutputConfig": An output config for the metric attribution.
• "removeMetrics": Remove metric attributes from the metric attribution.

update_recommender(recommender_arn, recommender_config)
update_recommender(recommender_arn, recommender_config, params::Dict{String,<:Any})

Updates the recommender to modify the recommender configuration. If you update the recommender to modify the columns used in training, Amazon Personalize automatically starts a full retraining of the models backing your recommender. While the update completes, you can still get recommendations from the recommender. The recommender uses the previous configuration until the update completes. To track the status of this update, use the latestRecommenderUpdate returned in the DescribeRecommender operation.

Arguments

• recommender_arn: The Amazon Resource Name (ARN) of the recommender to modify.
• recommender_config: The configuration details of the recommender.