Personalize

This page documents function available when using the Personalize module, created with @service Personalize.

Index

Documentation

Main.Personalize.create_batch_inference_jobMethod
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})

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

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.
  • "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.
Main.Personalize.create_batch_segment_jobMethod
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.
Main.Personalize.create_campaignMethod
create_campaign(name, solution_version_arn)
create_campaign(name, solution_version_arn, params::Dict{String,<:Any})

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 bill. We recommend starting with 1 for minProvisionedTPS (the default). Track your usage using Amazon CloudWatch metrics, and increase the minProvisionedTPS as necessary. A transaction is a single GetRecommendations or GetPersonalizedRanking call. Transactions per second (TPS) is the throughput and unit of billing for Amazon Personalize. The minimum provisioned TPS (minProvisionedTPS) specifies the baseline throughput provisioned by Amazon Personalize, and thus, the minimum billing charge. If your TPS increases beyond 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. The actual TPS used is calculated as the average requests/second within a 5-minute window. You pay for maximum of either the minimum provisioned TPS or the actual TPS. We recommend starting with a low minProvisionedTPS, track your usage using Amazon CloudWatch metrics, and then increase the minProvisionedTPS as necessary. Status A campaign can be in one of the following states: CREATE PENDING &gt; CREATE INPROGRESS &gt; ACTIVE -or- CREATE FAILED DELETE PENDING &gt; 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 solution version to deploy.

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.
Main.Personalize.create_datasetMethod
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 three types of datasets: Interactions Items Users Each dataset type has an associated schema with required field types. Only the 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 &gt; CREATE INPROGRESS &gt; ACTIVE -or- CREATE FAILED DELETE PENDING &gt; 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
  • 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.
Main.Personalize.create_dataset_export_jobMethod
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 &gt; CREATE IN_PROGRESS &gt; 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.
Main.Personalize.create_dataset_groupMethod
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: Interactions Items Users 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 &gt; CREATE IN_PROGRESS &gt; 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.
Main.Personalize.create_dataset_import_jobMethod
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. 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 &gt; CREATE IN_PROGRESS &gt; 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.
Main.Personalize.create_event_trackerMethod
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 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 &gt; CREATE INPROGRESS &gt; ACTIVE -or- CREATE FAILED DELETE PENDING &gt; 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.
Main.Personalize.create_filterMethod
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.
Main.Personalize.create_metric_attributionMethod
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.
Main.Personalize.create_recommenderMethod
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 &gt; CREATE INPROGRESS &gt; ACTIVE -or- CREATE FAILED STOP PENDING &gt; STOP INPROGRESS &gt; INACTIVE &gt; START PENDING &gt; START INPROGRESS &gt; ACTIVE DELETE PENDING &gt; 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.
Main.Personalize.create_schemaMethod
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.
Main.Personalize.create_solutionMethod
create_solution(dataset_group_arn, name)
create_solution(dataset_group_arn, name, params::Dict{String,<:Any})

Creates the configuration for training a model. A trained model is known as a solution version. After the configuration is created, you train the model (create a solution version) by calling the CreateSolutionVersion operation. Every time you call CreateSolutionVersion, a new version of the solution is created. After creating a solution version, you check its accuracy by calling GetSolutionMetrics. When you are satisfied with the version, you deploy it using CreateCampaign. The campaign provides recommendations to a client through the GetRecommendations API. To train a model, Amazon Personalize requires training data and a recipe. The training data comes from the dataset group that you provide in the request. A recipe specifies the training algorithm and a feature transformation. You can specify one of the predefined recipes provided by Amazon Personalize. 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 &gt; CREATE INPROGRESS &gt; ACTIVE -or- CREATE FAILED DELETE PENDING &gt; DELETE INPROGRESS To get the status of the solution, call DescribeSolution. Wait until the status shows as ACTIVE before calling 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 Determining your use case. 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.
  • "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 ARN of the recipe to use for model training. Only specified when performAutoML is false.
  • "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.
Main.Personalize.create_solution_versionMethod
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 FULL option trains the solution version based on the entirety of the input solution's training data, while the UPDATE option processes only the data that has changed in comparison to the input solution. Choose UPDATE when you want to incrementally update your solution version instead of creating an entirely new one. 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 HRNN-Coldstart recipe.
Main.Personalize.delete_campaignMethod
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.
Main.Personalize.delete_datasetMethod
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.
Main.Personalize.delete_dataset_groupMethod
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.
Main.Personalize.delete_event_trackerMethod
delete_event_tracker(event_tracker_arn)
delete_event_tracker(event_tracker_arn, params::Dict{String,<:Any})

Deletes the event tracker. Does not delete the event-interactions dataset from the associated 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.
Main.Personalize.delete_filterMethod
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.
Main.Personalize.delete_metric_attributionMethod
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).
Main.Personalize.delete_recommenderMethod
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.
Main.Personalize.delete_schemaMethod
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.
Main.Personalize.delete_solutionMethod
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.
Main.Personalize.describe_algorithmMethod
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.
Main.Personalize.describe_batch_inference_jobMethod
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.
Main.Personalize.describe_batch_segment_jobMethod
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.
Main.Personalize.describe_campaignMethod
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 &gt; CREATE INPROGRESS &gt; ACTIVE -or- CREATE FAILED DELETE PENDING &gt; 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.
Main.Personalize.describe_datasetMethod
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.
Main.Personalize.describe_dataset_export_jobMethod
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.
Main.Personalize.describe_dataset_groupMethod
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.
Main.Personalize.describe_dataset_import_jobMethod
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.
Main.Personalize.describe_event_trackerMethod
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.
Main.Personalize.describe_feature_transformationMethod
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.
Main.Personalize.describe_filterMethod
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.
Main.Personalize.describe_metric_attributionMethod
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).
Main.Personalize.describe_recipeMethod
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.
Main.Personalize.describe_recommenderMethod
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 &gt; CREATE INPROGRESS &gt; ACTIVE -or- CREATE FAILED STOP PENDING &gt; STOP INPROGRESS &gt; INACTIVE &gt; START PENDING &gt; START INPROGRESS &gt; ACTIVE DELETE PENDING &gt; 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.
Main.Personalize.describe_schemaMethod
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.
Main.Personalize.describe_solutionMethod
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.
Main.Personalize.describe_solution_versionMethod
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.
Main.Personalize.get_solution_metricsMethod
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.
Main.Personalize.list_batch_inference_jobsMethod
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.
Main.Personalize.list_batch_segment_jobsMethod
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.
Main.Personalize.list_campaignsMethod
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.
Main.Personalize.list_dataset_export_jobsMethod
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).
Main.Personalize.list_dataset_groupsMethod
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).
Main.Personalize.list_dataset_import_jobsMethod
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).
Main.Personalize.list_datasetsMethod
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 ListDatasetImportJobs for getting the next set of dataset import jobs (if they exist).
Main.Personalize.list_event_trackersMethod
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).
Main.Personalize.list_filtersMethod
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).
Main.Personalize.list_metric_attribution_metricsMethod
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.
Main.Personalize.list_metric_attributionsMethod
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.
Main.Personalize.list_recipesMethod
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.
Main.Personalize.list_recommendersMethod
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).
Main.Personalize.list_schemasMethod
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).
Main.Personalize.list_solution_versionsMethod
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.
Main.Personalize.list_solutionsMethod
list_solutions()
list_solutions(params::Dict{String,<:Any})

Returns a list of solutions that use the 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).
Main.Personalize.list_tags_for_resourceMethod
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.
Main.Personalize.start_recommenderMethod
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.
Main.Personalize.stop_recommenderMethod
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.
Main.Personalize.stop_solution_version_creationMethod
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 &gt; CREATESTOPPED or CREATEINPROGRESS &gt; CREATESTOPPING &gt; 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.
Main.Personalize.tag_resourceMethod
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 recources.
Main.Personalize.untag_resourceMethod
untag_resource(resource_arn, tag_keys)
untag_resource(resource_arn, tag_keys, params::Dict{String,<:Any})

Remove tags that are attached to a resource.

Arguments

  • resource_arn: The resource's Amazon Resource Name (ARN).
  • tag_keys: Keys to remove from the resource's tags.
Main.Personalize.update_campaignMethod
update_campaign(campaign_arn)
update_campaign(campaign_arn, params::Dict{String,<:Any})

Updates a campaign by either deploying a new solution or changing the value of the campaign's minProvisionedTPS parameter. 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 on campaigns, see CreateCampaign.

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 ARN of a new solution version to deploy.
Main.Personalize.update_metric_attributionMethod
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.
Main.Personalize.update_recommenderMethod
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.