aws_personalize
Amazon Personalize is a machine learning service that makes it easy to add individualized recommendations to customers.
Summary
Functions
-
create_batch_inference_job(Client, Input)
Creates a batch inference job.
- create_batch_inference_job(Client, Input, Options)
-
create_campaign(Client, Input)
Creates a campaign by deploying a solution version.
- create_campaign(Client, Input, Options)
-
create_dataset(Client, Input)
Creates an empty dataset and adds it to the specified dataset group.
- create_dataset(Client, Input, Options)
-
create_dataset_group(Client, Input)
Creates an empty dataset group.
- create_dataset_group(Client, Input, Options)
-
create_dataset_import_job(Client, Input)
Creates a job that imports training data from your data source (an Amazon S3 bucket) to an Amazon Personalize dataset.
- create_dataset_import_job(Client, Input, Options)
-
create_event_tracker(Client, Input)
Creates an event tracker that you use when adding event data to a specified dataset group using the PutEvents API.
- create_event_tracker(Client, Input, Options)
-
create_filter(Client, Input)
Creates a recommendation filter.
- create_filter(Client, Input, Options)
-
create_schema(Client, Input)
Creates an Amazon Personalize schema from the specified schema string.
- create_schema(Client, Input, Options)
-
create_solution(Client, Input)
Creates the configuration for training a model.
- create_solution(Client, Input, Options)
-
create_solution_version(Client, Input)
Trains or retrains an active solution.
- create_solution_version(Client, Input, Options)
-
delete_campaign(Client, Input)
Removes a campaign by deleting the solution deployment.
- delete_campaign(Client, Input, Options)
-
delete_dataset(Client, Input)
Deletes a dataset.
- delete_dataset(Client, Input, Options)
-
delete_dataset_group(Client, Input)
Deletes a dataset group.
- delete_dataset_group(Client, Input, Options)
-
delete_event_tracker(Client, Input)
Deletes the event tracker.
- delete_event_tracker(Client, Input, Options)
-
delete_filter(Client, Input)
Deletes a filter.
- delete_filter(Client, Input, Options)
-
delete_schema(Client, Input)
Deletes a schema.
- delete_schema(Client, Input, Options)
-
delete_solution(Client, Input)
Deletes all versions of a solution and the
Solution
object itself. - delete_solution(Client, Input, Options)
-
describe_algorithm(Client, Input)
Describes the given algorithm.
- describe_algorithm(Client, Input, Options)
-
describe_batch_inference_job(Client, Input)
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.
- describe_batch_inference_job(Client, Input, Options)
-
describe_campaign(Client, Input)
Describes the given campaign, including its status.
- describe_campaign(Client, Input, Options)
-
describe_dataset(Client, Input)
Describes the given dataset.
- describe_dataset(Client, Input, Options)
-
describe_dataset_group(Client, Input)
Describes the given dataset group.
- describe_dataset_group(Client, Input, Options)
-
describe_dataset_import_job(Client, Input)
Describes the dataset import job created by
CreateDatasetImportJob
, including the import job status. - describe_dataset_import_job(Client, Input, Options)
-
describe_event_tracker(Client, Input)
Describes an event tracker.
- describe_event_tracker(Client, Input, Options)
-
describe_feature_transformation(Client, Input)
Describes the given feature transformation.
- describe_feature_transformation(Client, Input, Options)
-
describe_filter(Client, Input)
Describes a filter's properties.
- describe_filter(Client, Input, Options)
-
describe_recipe(Client, Input)
Describes a recipe.
- describe_recipe(Client, Input, Options)
-
describe_schema(Client, Input)
Describes a schema.
- describe_schema(Client, Input, Options)
-
describe_solution(Client, Input)
Describes a solution.
- describe_solution(Client, Input, Options)
-
describe_solution_version(Client, Input)
Describes a specific version of a solution.
- describe_solution_version(Client, Input, Options)
-
get_solution_metrics(Client, Input)
Gets the metrics for the specified solution version.
- get_solution_metrics(Client, Input, Options)
-
list_batch_inference_jobs(Client, Input)
Gets a list of the batch inference jobs that have been performed off of a solution version.
- list_batch_inference_jobs(Client, Input, Options)
-
list_campaigns(Client, Input)
Returns a list of campaigns that use the given solution.
- list_campaigns(Client, Input, Options)
-
list_dataset_groups(Client, Input)
Returns a list of dataset groups.
- list_dataset_groups(Client, Input, Options)
-
list_dataset_import_jobs(Client, Input)
Returns a list of dataset import jobs that use the given dataset.
- list_dataset_import_jobs(Client, Input, Options)
-
list_datasets(Client, Input)
Returns the list of datasets contained in the given dataset group.
- list_datasets(Client, Input, Options)
-
list_event_trackers(Client, Input)
Returns the list of event trackers associated with the account.
- list_event_trackers(Client, Input, Options)
-
list_filters(Client, Input)
Lists all filters that belong to a given dataset group.
- list_filters(Client, Input, Options)
-
list_recipes(Client, Input)
Returns a list of available recipes.
- list_recipes(Client, Input, Options)
-
list_schemas(Client, Input)
Returns the list of schemas associated with the account.
- list_schemas(Client, Input, Options)
-
list_solution_versions(Client, Input)
Returns a list of solution versions for the given solution.
- list_solution_versions(Client, Input, Options)
-
list_solutions(Client, Input)
Returns a list of solutions that use the given dataset group.
- list_solutions(Client, Input, Options)
-
update_campaign(Client, Input)
Updates a campaign by either deploying a new solution or changing the value of the campaign's
minProvisionedTPS
parameter. - update_campaign(Client, Input, Options)
Functions
create_batch_inference_job(Client, Input)
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, seerecommendations-batch
.
create_batch_inference_job(Client, Input, Options)
create_campaign(Client, Input)
Creates a campaign by deploying 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 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 > 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.
Related APIs
ListCampaigns
DescribeCampaign
UpdateCampaign
DeleteCampaign
create_campaign(Client, Input, Options)
create_dataset(Client, Input)
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 > CREATE IN_PROGRESS > ACTIVE -or- CREATE FAILED
DELETE PENDING > DELETE IN_PROGRESS
To get the status of the dataset, call DescribeDataset
.
Related APIs
CreateDatasetGroup
ListDatasets
DescribeDataset
DeleteDataset
create_dataset(Client, Input, Options)
create_dataset_group(Client, Input)
Creates an empty dataset group.
A dataset group contains related datasets that supply data for training a model. A dataset group can contain at most three datasets, one for each type of dataset:
Interactions
Items
Users
To train a model (create a solution), a dataset group that
contains an Interactions
dataset is required. Call CreateDataset
to
add a dataset to the group.
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 AWS Key Management Service (KMS) key to encrypt the datasets in the group. If you specify a KMS key, you must also include an AWS 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
create_dataset_group(Client, Input, Options)
create_dataset_import_job(Client, Input)
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 AWS Identity and Access Management (IAM) role that has permission to read from the data source, as Amazon Personalize makes a copy of your data and processes it in an internal AWS system.
The dataset import job replaces any existing data in the dataset that you imported in bulk.
Status
A dataset import job can be in one of the following states:
CREATE PENDING > CREATE IN_PROGRESS > ACTIVE -or- CREATE FAILED
To get the status of the import job, call
DescribeDatasetImportJob
, providing the Amazon Resource Name (ARN) of
the dataset import job. The dataset import is complete when the status
shows as ACTIVE. If the status shows as CREATE FAILED, the response
includes a failureReason
key, which describes why the job failed.
Importing takes time. You must wait until the status shows as ACTIVE before training a model using the dataset.
Related APIs
ListDatasetImportJobs
DescribeDatasetImportJob
create_dataset_import_job(Client, Input, Options)
create_event_tracker(Client, Input)
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 > 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.
Related APIs
ListEventTrackers
DescribeEventTracker
DeleteEventTracker
create_event_tracker(Client, Input, Options)
create_filter(Client, Input)
Creates a recommendation filter.
For more information, seefilter
.
create_filter(Client, Input, Options)
create_schema(Client, Input)
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. You specify a schema when you call CreateDataset
.
Related APIs
ListSchemas
DescribeSchema
DeleteSchema
create_schema(Client, Input, Options)
create_solution(Client, Input)
Creates the configuration for training a model.
A trained model is known as a solution. After the configuration is
created, you train the model (create a solution) 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. Alternatively, you can specify performAutoML
and
Amazon Personalize will analyze your data and select the optimum
USER_PERSONALIZATION recipe for you.
Amazon Personalize doesn't support configuring the hpoObjective
for
solution hyperparameter optimization at this time.
Status
A solution can be in one of the following states:
CREATE PENDING > CREATE IN_PROGRESS > ACTIVE -or- CREATE FAILED
DELETE PENDING > DELETE IN_PROGRESS
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
create_solution(Client, Input, Options)
create_solution_version(Client, Input)
Trains or retrains an active solution.
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 -or- CREATE FAILED
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
create_solution_version(Client, Input, Options)
delete_campaign(Client, Input)
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 more information on campaigns, seeCreateCampaign
.
delete_campaign(Client, Input, Options)
delete_dataset(Client, Input)
Deletes a dataset.
You can't delete a dataset if an associatedDatasetImportJob
or
SolutionVersion
is in the CREATE PENDING or IN PROGRESS state. For more
information on datasets, see CreateDataset
.
delete_dataset(Client, Input, Options)
delete_dataset_group(Client, Input)
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.
delete_dataset_group(Client, Input, Options)
delete_event_tracker(Client, Input)
Deletes the event tracker.
Does not delete the event-interactions dataset from the associated dataset group. For more information on event trackers, seeCreateEventTracker
.
delete_event_tracker(Client, Input, Options)
delete_filter(Client, Input)
Deletes a filter.
delete_filter(Client, Input, Options)
delete_schema(Client, Input)
Deletes a schema.
Before deleting a schema, you must delete all datasets referencing the schema. For more information on schemas, seeCreateSchema
.
delete_schema(Client, Input, Options)
delete_solution(Client, Input)
Deletes all versions of a solution and the Solution
object itself.
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
.
delete_solution(Client, Input, Options)
describe_algorithm(Client, Input)
Describes the given algorithm.
describe_algorithm(Client, Input, Options)
describe_batch_inference_job(Client, Input)
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.
describe_batch_inference_job(Client, Input, Options)
describe_campaign(Client, Input)
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.
CreateCampaign
.
describe_campaign(Client, Input, Options)
describe_dataset(Client, Input)
Describes the given dataset.
For more information on datasets, seeCreateDataset
.
describe_dataset(Client, Input, Options)
describe_dataset_group(Client, Input)
Describes the given dataset group.
For more information on dataset groups, seeCreateDatasetGroup
.
describe_dataset_group(Client, Input, Options)
describe_dataset_import_job(Client, Input)
Describes the dataset import job created by CreateDatasetImportJob
,
including the import job status.
describe_dataset_import_job(Client, Input, Options)
describe_event_tracker(Client, Input)
Describes an event tracker.
The response includes thetrackingId
and status
of the event tracker.
For more information on event trackers, see CreateEventTracker
.
describe_event_tracker(Client, Input, Options)
describe_feature_transformation(Client, Input)
Describes the given feature transformation.
describe_feature_transformation(Client, Input, Options)
describe_filter(Client, Input)
Describes a filter's properties.
describe_filter(Client, Input, Options)
describe_recipe(Client, Input)
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.
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.
describe_recipe(Client, Input, Options)
describe_schema(Client, Input)
Describes a schema.
For more information on schemas, seeCreateSchema
.
describe_schema(Client, Input, Options)
describe_solution(Client, Input)
Describes a solution.
For more information on solutions, seeCreateSolution
.
describe_solution(Client, Input, Options)
describe_solution_version(Client, Input)
Describes a specific version of a solution.
For more information on solutions, seeCreateSolution
.
describe_solution_version(Client, Input, Options)
get_solution_metrics(Client, Input)
Gets the metrics for the specified solution version.
get_solution_metrics(Client, Input, Options)
list_batch_inference_jobs(Client, Input)
Gets a list of the batch inference jobs that have been performed off of a solution version.
list_batch_inference_jobs(Client, Input, Options)
list_campaigns(Client, Input)
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, seeCreateCampaign
.
list_campaigns(Client, Input, Options)
list_dataset_groups(Client, Input)
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, seeCreateDatasetGroup
.
list_dataset_groups(Client, Input, Options)
list_dataset_import_jobs(Client, Input)
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, seeCreateDatasetImportJob
. For more
information on datasets, see CreateDataset
.
list_dataset_import_jobs(Client, Input, Options)
list_datasets(Client, Input)
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, seeCreateDataset
.
list_datasets(Client, Input, Options)
list_event_trackers(Client, Input)
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, seeCreateEventTracker
.
list_event_trackers(Client, Input, Options)
list_filters(Client, Input)
Lists all filters that belong to a given dataset group.
list_filters(Client, Input, Options)
list_recipes(Client, Input)
Returns a list of available recipes.
The response provides the properties for each recipe, including the recipe's Amazon Resource Name (ARN).list_recipes(Client, Input, Options)
list_schemas(Client, Input)
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, seeCreateSchema
.
list_schemas(Client, Input, Options)
list_solution_versions(Client, Input)
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). For more information on solutions, seeCreateSolution
.
list_solution_versions(Client, Input, Options)
list_solutions(Client, Input)
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, seeCreateSolution
.
list_solutions(Client, Input, Options)
update_campaign(Client, Input)
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
API.
You must wait until the status
of the updated campaign is ACTIVE
before asking the campaign for recommendations.
CreateCampaign
.