View Source AWS.Personalize (aws-elixir v1.0.4)
Amazon Personalize is a machine learning service that makes it easy to add individualized recommendations to customers.
Link to this section Summary
Functions
Generates batch recommendations based on a list of items or users stored in Amazon S3 and exports the recommendations to an Amazon S3 bucket.
Creates a batch segment job.
You incur campaign costs while it is active.
Creates a batch job that deletes all references to specific users from an Amazon Personalize dataset group in batches.
Creates an empty dataset and adds it to the specified dataset group.
Creates a job that exports data from your dataset to an Amazon S3 bucket.
Creates an empty dataset group.
Creates a job that imports training data from your data source (an Amazon S3 bucket) to an Amazon Personalize dataset.
Creates an event tracker that you use when adding event data to a specified dataset group using the PutEvents API.
Creates a recommendation filter.
Creates a metric attribution.
Creates a recommender with the recipe (a Domain dataset group use case) you specify.
Creates an Amazon Personalize schema from the specified schema string.
By default, all new solutions use automatic training.
Trains or retrains an active solution in a Custom dataset group.
Removes a campaign by deleting the solution deployment.
Deletes a dataset.
Deletes a dataset group.
Deletes the event tracker.
Deletes a filter.
Deletes a metric attribution.
Deactivates and removes a recommender.
Deletes a schema.
Deletes all versions of a solution and the Solution object itself.
Describes the given algorithm.
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.
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.
Describes the given campaign, including its status.
Describes the data deletion job created by CreateDataDeletionJob, including the job status.
Describes the given dataset.
Describes the dataset export job created by CreateDatasetExportJob, including the export job status.
Describes the given dataset group.
Describes the dataset import job created by CreateDatasetImportJob, including the import job status.
Describes an event tracker.
Describes the given feature transformation.
Describes a filter's properties.
Describes a metric attribution.
Describes a recipe.
Describes the given recommender, including its status.
Describes a schema.
Describes a solution.
Describes a specific version of a solution.
Gets the metrics for the specified solution version.
Gets a list of the batch inference jobs that have been performed off of a solution version.
Gets a list of the batch segment jobs that have been performed off of a solution version that you specify.
Returns a list of campaigns that use the given solution.
Returns a list of data deletion jobs for a dataset group ordered by creation time, with the most recent first.
Returns a list of dataset export jobs that use the given dataset.
Returns a list of dataset groups.
Returns a list of dataset import jobs that use the given dataset.
Returns the list of datasets contained in the given dataset group.
Returns the list of event trackers associated with the account.
Lists all filters that belong to a given dataset group.
Lists the metrics for the metric attribution.
Lists metric attributions.
Returns a list of available recipes.
Returns a list of recommenders in a given Domain dataset group.
Returns the list of schemas associated with the account.
Returns a list of solution versions for the given solution.
Returns a list of solutions in a given dataset group.
Get a list of tags attached to a resource.
Starts a recommender that is INACTIVE.
Stops a recommender that is ACTIVE.
Stops creating a solution version that is in a state of CREATE_PENDING or CREATE IN_PROGRESS.
Add a list of tags to a resource.
Removes the specified tags that are attached to a resource.
Updates a campaign to deploy a retrained solution version with an existing
campaign, change your campaign's minProvisionedTPS,
or modify your campaign's configuration.
Update a dataset to replace its schema with a new or existing one.
Updates a metric attribution.
Updates the recommender to modify the recommender configuration.
Updates an Amazon Personalize solution to use a different automatic training configuration.
Link to this section Functions
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.
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.
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
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
Status
A campaign can be in one of the following states:
* CREATE PENDING > CREATE IN_PROGRESS > ACTIVE -or- CREATE FAILED
* DELETE PENDING > 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.
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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.
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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
Status
A data deletion job can have one of the following statuses:
* PENDING > IN_PROGRESS > 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.
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Creates an empty dataset and adds it to the specified dataset group.
Use CreateDatasetImportJob to import your training data to a dataset.
There are 5 types of datasets:
* Item interactions
* Items
* Users
* Action interactions
* Actions
Each dataset type has an associated schema with required field types.
Only the Item interactions dataset is required in order to train a
model (also referred to as creating a solution).
A dataset can be in one of the following states:
* CREATE PENDING > CREATE IN_PROGRESS > ACTIVE -or- CREATE FAILED
* DELETE PENDING > DELETE IN_PROGRESS
To get the status of the dataset, call DescribeDataset.
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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
Status
A dataset export job can be in one of the following states:
* CREATE PENDING > CREATE IN_PROGRESS > ACTIVE -or- CREATE FAILED
To get the status of the export job, call
DescribeDatasetExportJob,
and specify the Amazon Resource Name
(ARN) of the dataset export job. The dataset export is complete when the
status shows as ACTIVE. If the status shows as CREATE FAILED, the response
includes a failureReason key, which describes why the job
failed.
Creates an empty dataset group.
A dataset group is a container for Amazon Personalize resources. A dataset group can contain at most three datasets, one for each type of dataset:
* Item interactions
* Items
* Users
* Actions
* Action interactions
A dataset group can be a Domain dataset group, where you specify a domain and use pre-configured resources like recommenders, or a Custom dataset group, where you use custom resources, such as a solution with a solution version, that you deploy with a campaign. If you start with a Domain dataset group, you can still add custom resources such as solutions and solution versions trained with recipes for custom use cases and deployed with campaigns.
A dataset group can be in one of the following states:
* CREATE PENDING > CREATE IN_PROGRESS > ACTIVE -or- CREATE FAILED
* DELETE PENDING
To get the status of the dataset group, call
DescribeDatasetGroup. If the status shows as CREATE FAILED, the
response includes a failureReason key, which describes why
the creation failed.
You must wait until the status of the dataset group is
ACTIVE before adding a dataset to the group.
You can specify an Key Management Service (KMS) key to encrypt the datasets in the group. If you specify a KMS key, you must also include an Identity and Access Management (IAM) role that has permission to access the key.
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APIs that require a dataset group ARN in the request
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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
Status
A dataset import job can be in one of the following states:
* CREATE PENDING > CREATE IN_PROGRESS > ACTIVE -or- CREATE FAILED
To get the status of the import job, call
DescribeDatasetImportJob, providing the Amazon Resource Name
(ARN) of the dataset import job. The dataset import is complete when the
status shows as ACTIVE. If the status shows as CREATE FAILED, the response
includes a failureReason key, which describes why the job
failed.
Importing takes time. You must wait until the status shows as ACTIVE before training a model using the dataset.
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Creates an event tracker that you use when adding event data to a specified dataset group using the PutEvents API.
Only one event tracker can be associated with a dataset group. You will get
an error if you call CreateEventTracker using the same dataset group as an
existing event tracker.
When you create an event tracker, the response includes a tracking ID, which you pass as a parameter when you use the PutEvents operation. Amazon Personalize then appends the event data to the Item interactions dataset of the dataset group you specify in your event tracker.
The event tracker can be in one of the following states:
* CREATE PENDING > CREATE IN_PROGRESS > ACTIVE -or- CREATE FAILED
* DELETE PENDING > 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.
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Creates a recommendation filter.
For more information, see Filtering recommendations and user segments.
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.
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
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
Status
A recommender can be in one of the following states:
* CREATE PENDING > CREATE IN_PROGRESS > ACTIVE -or- CREATE FAILED
* STOP PENDING > STOP IN_PROGRESS > INACTIVE > START PENDING > START IN_PROGRESS > ACTIVE
* DELETE PENDING > 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.
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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.
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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
Status
A solution can be in one of the following states:
* CREATE PENDING > CREATE IN_PROGRESS > ACTIVE -or- CREATE FAILED
* DELETE PENDING > 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.
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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
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.
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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.
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.
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.
Deletes the event tracker.
Does not delete the dataset from the dataset group. For more information on event trackers, see CreateEventTracker.
Deletes a filter.
Deletes a metric attribution.
Deactivates and removes a recommender.
A deleted recommender can no longer be specified in a GetRecommendations request.
Deletes a schema.
Before deleting a schema, you must delete all datasets referencing the schema. For more information on schemas, see CreateSchema.
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.
Describes the given algorithm.
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.
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.
Describes the given campaign, including its status.
A campaign can be in one of the following states:
* CREATE PENDING > CREATE IN_PROGRESS > ACTIVE -or- CREATE FAILED
* DELETE PENDING > DELETE IN_PROGRESS
When the status is CREATE FAILED, the response includes the
failureReason key, which describes why.
For more information on campaigns, see CreateCampaign.
Describes the data deletion job created by CreateDataDeletionJob, including the job status.
Describes the given dataset.
For more information on datasets, see CreateDataset.
Describes the dataset export job created by CreateDatasetExportJob, including the export job status.
Describes the given dataset group.
For more information on dataset groups, see CreateDatasetGroup.
Describes the dataset import job created by CreateDatasetImportJob, including the import job status.
Describes an event tracker.
The response includes the trackingId and
status of the event tracker.
For more information on event trackers, see
CreateEventTracker.
Describes the given feature transformation.
Describes a filter's properties.
Describes a metric attribution.
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.
Describes the given recommender, including its status.
A recommender can be in one of the following states:
* CREATE PENDING > CREATE IN_PROGRESS > ACTIVE -or- CREATE FAILED
* STOP PENDING > STOP IN_PROGRESS > INACTIVE > START PENDING > START IN_PROGRESS > ACTIVE
* DELETE PENDING > DELETE IN_PROGRESS
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.
Describes a schema.
For more information on schemas, see CreateSchema.
Describes a solution.
For more information on solutions, see CreateSolution.
Describes a specific version of a solution.
For more information on solutions, see CreateSolution
Gets the metrics for the specified solution version.
Gets a list of the batch inference jobs that have been performed off of a solution version.
Gets a list of the batch segment jobs that have been performed off of a solution version that you specify.
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.
Returns a list of data deletion jobs for a dataset group ordered by creation time, with the most recent first.
When a dataset group is not specified, all the data deletion jobs associated with the account are listed. The response provides the properties for each job, including the Amazon Resource Name (ARN). For more information on data deletion jobs, see Deleting users.
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.
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.
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.
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.
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.
Lists all filters that belong to a given dataset group.
Lists the metrics for the metric attribution.
Lists metric attributions.
Returns a list of available recipes.
The response provides the properties for each recipe, including the recipe's Amazon Resource Name (ARN).
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.
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.
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).
Returns a list of solutions in a given dataset group.
When a dataset group is not specified, all the solutions associated with the account are listed. The response provides the properties for each solution, including the Amazon Resource Name (ARN). For more information on solutions, see CreateSolution.
Get a list of tags attached to a resource.
Starts a recommender that is INACTIVE.
Starting a recommender does not create any new models, but resumes billing and automatic retraining for the recommender.
Stops a recommender that is ACTIVE.
Stopping a recommender halts billing and automatic retraining for the recommender.
Stops creating a solution version that is in a state of CREATE_PENDING or CREATE IN_PROGRESS.
Depending on the current state of the solution version, the solution version state changes as follows:
* CREATE_PENDING > CREATE_STOPPED
or
* CREATE_IN_PROGRESS > CREATE_STOPPING > CREATE_STOPPED
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.
Add a list of tags to a resource.
Removes the specified tags that are attached to a resource.
For more information, see Removing tags from Amazon Personalize resources.
Updates a campaign to deploy a retrained solution version with an existing
campaign, change your campaign's minProvisionedTPS,
or modify your campaign's configuration.
For example, you can set enableMetadataWithRecommendations to true for an
existing campaign.
To update a campaign to start automatically using the latest solution version, specify the following:
*
For the SolutionVersionArn parameter, specify the Amazon Resource Name (ARN)
of your solution in
SolutionArn/$LATEST format.
*
In the campaignConfig, set syncWithLatestSolutionVersion to true.
To update a campaign, the campaign status must be ACTIVE or CREATE FAILED. Check the campaign status using the DescribeCampaign operation.
You can still get recommendations from a campaign while an update is in
progress.
The campaign will use the previous solution version and campaign configuration
to generate recommendations until the latest campaign update status is Active.
For more information about updating a campaign, including code samples, see Updating a campaign. For more information about campaigns, see Creating a campaign.
Update a dataset to replace its schema with a new or existing one.
For more information, see Replacing a dataset's schema.
Updates a metric attribution.
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.
Updates an Amazon Personalize solution to use a different automatic training configuration.
When you update a solution, you can change whether the solution uses automatic training, and you can change the training frequency. For more information about updating a solution, see Updating a solution.
A solution update can be in one of the following states:
CREATE PENDING > CREATE IN_PROGRESS > ACTIVE -or- CREATE FAILED
To get the status of a solution update, call the
DescribeSolution
API operation and find the status
in the latestSolutionUpdate.