aws-sdk/clients/personalize.d.ts

AWS SDK for JavaScript

import {Request} from '../lib/request';
import {Response} from '../lib/response';
import {AWSError} from '../lib/error';
import {Service} from '../lib/service';
import {ServiceConfigurationOptions} from '../lib/service';
import {ConfigBase as Config} from '../lib/config-base';
interface Blob {}
declare class Personalize extends Service {
  /**
   * Constructs a service object. This object has one method for each API operation.
   */
  constructor(options?: Personalize.Types.ClientConfiguration)
  config: Config & Personalize.Types.ClientConfiguration;
  /**
   * 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.
   */
  createBatchInferenceJob(params: Personalize.Types.CreateBatchInferenceJobRequest, callback?: (err: AWSError, data: Personalize.Types.CreateBatchInferenceJobResponse) => void): Request<Personalize.Types.CreateBatchInferenceJobResponse, AWSError>;
  /**
   * 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.
   */
  createBatchInferenceJob(callback?: (err: AWSError, data: Personalize.Types.CreateBatchInferenceJobResponse) => void): Request<Personalize.Types.CreateBatchInferenceJobResponse, AWSError>;
  /**
   * 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.
   */
  createBatchSegmentJob(params: Personalize.Types.CreateBatchSegmentJobRequest, callback?: (err: AWSError, data: Personalize.Types.CreateBatchSegmentJobResponse) => void): Request<Personalize.Types.CreateBatchSegmentJobResponse, AWSError>;
  /**
   * 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.
   */
  createBatchSegmentJob(callback?: (err: AWSError, data: Personalize.Types.CreateBatchSegmentJobResponse) => void): Request<Personalize.Types.CreateBatchSegmentJobResponse, AWSError>;
  /**
   *   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 &gt; CREATE IN_PROGRESS &gt; ACTIVE -or- CREATE FAILED   DELETE PENDING &gt; DELETE IN_PROGRESS   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   
   */
  createCampaign(params: Personalize.Types.CreateCampaignRequest, callback?: (err: AWSError, data: Personalize.Types.CreateCampaignResponse) => void): Request<Personalize.Types.CreateCampaignResponse, AWSError>;
  /**
   *   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 &gt; CREATE IN_PROGRESS &gt; ACTIVE -or- CREATE FAILED   DELETE PENDING &gt; DELETE IN_PROGRESS   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   
   */
  createCampaign(callback?: (err: AWSError, data: Personalize.Types.CreateCampaignResponse) => void): Request<Personalize.Types.CreateCampaignResponse, AWSError>;
  /**
   * 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 USER_ID 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 &gt; IN_PROGRESS &gt; 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   
   */
  createDataDeletionJob(params: Personalize.Types.CreateDataDeletionJobRequest, callback?: (err: AWSError, data: Personalize.Types.CreateDataDeletionJobResponse) => void): Request<Personalize.Types.CreateDataDeletionJobResponse, AWSError>;
  /**
   * 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 USER_ID 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 &gt; IN_PROGRESS &gt; 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   
   */
  createDataDeletionJob(callback?: (err: AWSError, data: Personalize.Types.CreateDataDeletionJobResponse) => void): Request<Personalize.Types.CreateDataDeletionJobResponse, AWSError>;
  /**
   * 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 &gt; CREATE IN_PROGRESS &gt; ACTIVE -or- CREATE FAILED   DELETE PENDING &gt; DELETE IN_PROGRESS   To get the status of the dataset, call DescribeDataset.  Related APIs     CreateDatasetGroup     ListDatasets     DescribeDataset     DeleteDataset   
   */
  createDataset(params: Personalize.Types.CreateDatasetRequest, callback?: (err: AWSError, data: Personalize.Types.CreateDatasetResponse) => void): Request<Personalize.Types.CreateDatasetResponse, AWSError>;
  /**
   * 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 &gt; CREATE IN_PROGRESS &gt; ACTIVE -or- CREATE FAILED   DELETE PENDING &gt; DELETE IN_PROGRESS   To get the status of the dataset, call DescribeDataset.  Related APIs     CreateDatasetGroup     ListDatasets     DescribeDataset     DeleteDataset   
   */
  createDataset(callback?: (err: AWSError, data: Personalize.Types.CreateDatasetResponse) => void): Request<Personalize.Types.CreateDatasetResponse, AWSError>;
  /**
   *  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. 
   */
  createDatasetExportJob(params: Personalize.Types.CreateDatasetExportJobRequest, callback?: (err: AWSError, data: Personalize.Types.CreateDatasetExportJobResponse) => void): Request<Personalize.Types.CreateDatasetExportJobResponse, AWSError>;
  /**
   *  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. 
   */
  createDatasetExportJob(callback?: (err: AWSError, data: Personalize.Types.CreateDatasetExportJobResponse) => void): Request<Personalize.Types.CreateDatasetExportJobResponse, AWSError>;
  /**
   * 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 &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   
   */
  createDatasetGroup(params: Personalize.Types.CreateDatasetGroupRequest, callback?: (err: AWSError, data: Personalize.Types.CreateDatasetGroupResponse) => void): Request<Personalize.Types.CreateDatasetGroupResponse, AWSError>;
  /**
   * 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 &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   
   */
  createDatasetGroup(callback?: (err: AWSError, data: Personalize.Types.CreateDatasetGroupResponse) => void): Request<Personalize.Types.CreateDatasetGroupResponse, AWSError>;
  /**
   * 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 &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   
   */
  createDatasetImportJob(params: Personalize.Types.CreateDatasetImportJobRequest, callback?: (err: AWSError, data: Personalize.Types.CreateDatasetImportJobResponse) => void): Request<Personalize.Types.CreateDatasetImportJobResponse, AWSError>;
  /**
   * 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 &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   
   */
  createDatasetImportJob(callback?: (err: AWSError, data: Personalize.Types.CreateDatasetImportJobResponse) => void): Request<Personalize.Types.CreateDatasetImportJobResponse, AWSError>;
  /**
   * 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 &gt; CREATE IN_PROGRESS &gt; ACTIVE -or- CREATE FAILED   DELETE PENDING &gt; DELETE IN_PROGRESS   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   
   */
  createEventTracker(params: Personalize.Types.CreateEventTrackerRequest, callback?: (err: AWSError, data: Personalize.Types.CreateEventTrackerResponse) => void): Request<Personalize.Types.CreateEventTrackerResponse, AWSError>;
  /**
   * 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 &gt; CREATE IN_PROGRESS &gt; ACTIVE -or- CREATE FAILED   DELETE PENDING &gt; DELETE IN_PROGRESS   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   
   */
  createEventTracker(callback?: (err: AWSError, data: Personalize.Types.CreateEventTrackerResponse) => void): Request<Personalize.Types.CreateEventTrackerResponse, AWSError>;
  /**
   * Creates a recommendation filter. For more information, see Filtering recommendations and user segments.
   */
  createFilter(params: Personalize.Types.CreateFilterRequest, callback?: (err: AWSError, data: Personalize.Types.CreateFilterResponse) => void): Request<Personalize.Types.CreateFilterResponse, AWSError>;
  /**
   * Creates a recommendation filter. For more information, see Filtering recommendations and user segments.
   */
  createFilter(callback?: (err: AWSError, data: Personalize.Types.CreateFilterResponse) => void): Request<Personalize.Types.CreateFilterResponse, AWSError>;
  /**
   * 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.
   */
  createMetricAttribution(params: Personalize.Types.CreateMetricAttributionRequest, callback?: (err: AWSError, data: Personalize.Types.CreateMetricAttributionResponse) => void): Request<Personalize.Types.CreateMetricAttributionResponse, AWSError>;
  /**
   * 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.
   */
  createMetricAttribution(callback?: (err: AWSError, data: Personalize.Types.CreateMetricAttributionResponse) => void): Request<Personalize.Types.CreateMetricAttributionResponse, AWSError>;
  /**
   * 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 IN_PROGRESS &gt; ACTIVE -or- CREATE FAILED   STOP PENDING &gt; STOP IN_PROGRESS &gt; INACTIVE &gt; START PENDING &gt; START IN_PROGRESS &gt; ACTIVE   DELETE PENDING &gt; DELETE IN_PROGRESS   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   
   */
  createRecommender(params: Personalize.Types.CreateRecommenderRequest, callback?: (err: AWSError, data: Personalize.Types.CreateRecommenderResponse) => void): Request<Personalize.Types.CreateRecommenderResponse, AWSError>;
  /**
   * 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 IN_PROGRESS &gt; ACTIVE -or- CREATE FAILED   STOP PENDING &gt; STOP IN_PROGRESS &gt; INACTIVE &gt; START PENDING &gt; START IN_PROGRESS &gt; ACTIVE   DELETE PENDING &gt; DELETE IN_PROGRESS   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   
   */
  createRecommender(callback?: (err: AWSError, data: Personalize.Types.CreateRecommenderResponse) => void): Request<Personalize.Types.CreateRecommenderResponse, AWSError>;
  /**
   * 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   
   */
  createSchema(params: Personalize.Types.CreateSchemaRequest, callback?: (err: AWSError, data: Personalize.Types.CreateSchemaResponse) => void): Request<Personalize.Types.CreateSchemaResponse, AWSError>;
  /**
   * 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   
   */
  createSchema(callback?: (err: AWSError, data: Personalize.Types.CreateSchemaResponse) => void): Request<Personalize.Types.CreateSchemaResponse, AWSError>;
  /**
   *  By default, all new solutions use automatic training. With automatic training, you incur training costs while your solution is active. To avoid unnecessary costs, when you are finished you can update the solution to turn off automatic training. For information about training costs, see Amazon Personalize pricing.  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 within one hour after the solution is ACTIVE. If you manually create a solution version within the hour, the solution skips the first automatic training. 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 &gt; CREATE IN_PROGRESS &gt; ACTIVE -or- CREATE FAILED   DELETE PENDING &gt; DELETE IN_PROGRESS   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     UpdateSolution     ListSolutions     CreateSolutionVersion     DescribeSolution     DeleteSolution       ListSolutionVersions     DescribeSolutionVersion   
   */
  createSolution(params: Personalize.Types.CreateSolutionRequest, callback?: (err: AWSError, data: Personalize.Types.CreateSolutionResponse) => void): Request<Personalize.Types.CreateSolutionResponse, AWSError>;
  /**
   *  By default, all new solutions use automatic training. With automatic training, you incur training costs while your solution is active. To avoid unnecessary costs, when you are finished you can update the solution to turn off automatic training. For information about training costs, see Amazon Personalize pricing.  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 within one hour after the solution is ACTIVE. If you manually create a solution version within the hour, the solution skips the first automatic training. 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 &gt; CREATE IN_PROGRESS &gt; ACTIVE -or- CREATE FAILED   DELETE PENDING &gt; DELETE IN_PROGRESS   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     UpdateSolution     ListSolutions     CreateSolutionVersion     DescribeSolution     DeleteSolution       ListSolutionVersions     DescribeSolutionVersion   
   */
  createSolution(callback?: (err: AWSError, data: Personalize.Types.CreateSolutionResponse) => void): Request<Personalize.Types.CreateSolutionResponse, AWSError>;
  /**
   * 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   
   */
  createSolutionVersion(params: Personalize.Types.CreateSolutionVersionRequest, callback?: (err: AWSError, data: Personalize.Types.CreateSolutionVersionResponse) => void): Request<Personalize.Types.CreateSolutionVersionResponse, AWSError>;
  /**
   * 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   
   */
  createSolutionVersion(callback?: (err: AWSError, data: Personalize.Types.CreateSolutionVersionResponse) => void): Request<Personalize.Types.CreateSolutionVersionResponse, AWSError>;
  /**
   * 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.
   */
  deleteCampaign(params: Personalize.Types.DeleteCampaignRequest, callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * 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.
   */
  deleteCampaign(callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * 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.
   */
  deleteDataset(params: Personalize.Types.DeleteDatasetRequest, callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * 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.
   */
  deleteDataset(callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * 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.  
   */
  deleteDatasetGroup(params: Personalize.Types.DeleteDatasetGroupRequest, callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * 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.  
   */
  deleteDatasetGroup(callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Deletes the event tracker. Does not delete the dataset from the dataset group. For more information on event trackers, see CreateEventTracker.
   */
  deleteEventTracker(params: Personalize.Types.DeleteEventTrackerRequest, callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Deletes the event tracker. Does not delete the dataset from the dataset group. For more information on event trackers, see CreateEventTracker.
   */
  deleteEventTracker(callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Deletes a filter.
   */
  deleteFilter(params: Personalize.Types.DeleteFilterRequest, callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Deletes a filter.
   */
  deleteFilter(callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Deletes a metric attribution.
   */
  deleteMetricAttribution(params: Personalize.Types.DeleteMetricAttributionRequest, callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Deletes a metric attribution.
   */
  deleteMetricAttribution(callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Deactivates and removes a recommender. A deleted recommender can no longer be specified in a GetRecommendations request.
   */
  deleteRecommender(params: Personalize.Types.DeleteRecommenderRequest, callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Deactivates and removes a recommender. A deleted recommender can no longer be specified in a GetRecommendations request.
   */
  deleteRecommender(callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Deletes a schema. Before deleting a schema, you must delete all datasets referencing the schema. For more information on schemas, see CreateSchema.
   */
  deleteSchema(params: Personalize.Types.DeleteSchemaRequest, callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Deletes a schema. Before deleting a schema, you must delete all datasets referencing the schema. For more information on schemas, see CreateSchema.
   */
  deleteSchema(callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * 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.
   */
  deleteSolution(params: Personalize.Types.DeleteSolutionRequest, callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * 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.
   */
  deleteSolution(callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Describes the given algorithm.
   */
  describeAlgorithm(params: Personalize.Types.DescribeAlgorithmRequest, callback?: (err: AWSError, data: Personalize.Types.DescribeAlgorithmResponse) => void): Request<Personalize.Types.DescribeAlgorithmResponse, AWSError>;
  /**
   * Describes the given algorithm.
   */
  describeAlgorithm(callback?: (err: AWSError, data: Personalize.Types.DescribeAlgorithmResponse) => void): Request<Personalize.Types.DescribeAlgorithmResponse, AWSError>;
  /**
   * 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.
   */
  describeBatchInferenceJob(params: Personalize.Types.DescribeBatchInferenceJobRequest, callback?: (err: AWSError, data: Personalize.Types.DescribeBatchInferenceJobResponse) => void): Request<Personalize.Types.DescribeBatchInferenceJobResponse, AWSError>;
  /**
   * 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.
   */
  describeBatchInferenceJob(callback?: (err: AWSError, data: Personalize.Types.DescribeBatchInferenceJobResponse) => void): Request<Personalize.Types.DescribeBatchInferenceJobResponse, AWSError>;
  /**
   * 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.
   */
  describeBatchSegmentJob(params: Personalize.Types.DescribeBatchSegmentJobRequest, callback?: (err: AWSError, data: Personalize.Types.DescribeBatchSegmentJobResponse) => void): Request<Personalize.Types.DescribeBatchSegmentJobResponse, AWSError>;
  /**
   * 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.
   */
  describeBatchSegmentJob(callback?: (err: AWSError, data: Personalize.Types.DescribeBatchSegmentJobResponse) => void): Request<Personalize.Types.DescribeBatchSegmentJobResponse, AWSError>;
  /**
   * Describes the given campaign, including its status. A campaign can be in one of the following states:   CREATE PENDING &gt; CREATE IN_PROGRESS &gt; ACTIVE -or- CREATE FAILED   DELETE PENDING &gt; DELETE IN_PROGRESS   When the status is CREATE