2026/04/16 - Amazon Connect Customer Profiles - 4 new7 updated api methods
Changes Amazon Connect Customer Profiles adds RecommenderSchema CRUD APIs for custom ML training columns. CreateRecommender and CreateRecommenderFilter now accept optional RecommenderSchemaName.
Retrieves information about a specific recommender schema in a domain.
See also: AWS API Documentation
Request Syntax
client.get_recommender_schema(
DomainName='string',
RecommenderSchemaName='string'
)
string
[REQUIRED]
The unique name of the domain.
string
[REQUIRED]
The name of the recommender schema to retrieve.
dict
Response Syntax
{
'RecommenderSchemaName': 'string',
'Fields': {
'string': [
{
'TargetFieldName': 'string',
'ContentType': 'STRING'|'NUMBER',
'FeatureType': 'TEXTUAL'|'CATEGORICAL'
},
]
},
'CreatedAt': datetime(2015, 1, 1),
'Status': 'ACTIVE'|'DELETING'
}
Response Structure
(dict) --
RecommenderSchemaName (string) --
The name of the recommender schema.
Fields (dict) --
A map of dataset type to column definitions included in the schema.
(string) --
(list) --
(dict) --
Defines a column in a recommender schema, including the target field name and optional feature and content type settings for training.
TargetFieldName (string) --
The name of the target field in the dataset, such as Location.City or Attributes.MealTime.
ContentType (string) --
The data type of the column value. Valid values are String and Number. The default value is String.
FeatureType (string) --
How the column is treated for model training. Valid values are CATEGORICAL and TEXTUAL.
CreatedAt (datetime) --
The timestamp of when the recommender schema was created.
Status (string) --
The status of the recommender schema.
Creates a recommender schema. A recommender schema defines the set of data columns available for training recommenders and filters under a domain.
See also: AWS API Documentation
Request Syntax
client.create_recommender_schema(
DomainName='string',
RecommenderSchemaName='string',
Fields={
'string': [
{
'TargetFieldName': 'string',
'ContentType': 'STRING'|'NUMBER',
'FeatureType': 'TEXTUAL'|'CATEGORICAL'
},
]
},
Tags={
'string': 'string'
}
)
string
[REQUIRED]
The unique name of the domain.
string
[REQUIRED]
The name of the recommender schema. The name must be unique within the domain.
dict
[REQUIRED]
A map of dataset type to column definitions that specifies which data columns to include in the schema. Currently only the _webAnalytics key is supported.
(string) --
(list) --
(dict) --
Defines a column in a recommender schema, including the target field name and optional feature and content type settings for training.
TargetFieldName (string) -- [REQUIRED]
The name of the target field in the dataset, such as Location.City or Attributes.MealTime.
ContentType (string) --
The data type of the column value. Valid values are String and Number. The default value is String.
FeatureType (string) --
How the column is treated for model training. Valid values are CATEGORICAL and TEXTUAL.
dict
The tags used to organize, track, or control access for this resource.
(string) --
(string) --
dict
Response Syntax
{
'RecommenderSchemaArn': 'string',
'RecommenderSchemaName': 'string',
'Fields': {
'string': [
{
'TargetFieldName': 'string',
'ContentType': 'STRING'|'NUMBER',
'FeatureType': 'TEXTUAL'|'CATEGORICAL'
},
]
},
'CreatedAt': datetime(2015, 1, 1),
'Status': 'ACTIVE'|'DELETING',
'Tags': {
'string': 'string'
}
}
Response Structure
(dict) --
RecommenderSchemaArn (string) --
The Amazon Resource Name (ARN) of the recommender schema.
RecommenderSchemaName (string) --
The name of the recommender schema.
Fields (dict) --
A map of dataset type to column definitions included in the schema.
(string) --
(list) --
(dict) --
Defines a column in a recommender schema, including the target field name and optional feature and content type settings for training.
TargetFieldName (string) --
The name of the target field in the dataset, such as Location.City or Attributes.MealTime.
ContentType (string) --
The data type of the column value. Valid values are String and Number. The default value is String.
FeatureType (string) --
How the column is treated for model training. Valid values are CATEGORICAL and TEXTUAL.
CreatedAt (datetime) --
The timestamp of when the recommender schema was created.
Status (string) --
The status of the recommender schema.
Tags (dict) --
The tags used to organize, track, or control access for this resource.
(string) --
(string) --
Returns a list of recommender schemas in the specified domain.
See also: AWS API Documentation
Request Syntax
client.list_recommender_schemas(
DomainName='string',
MaxResults=123,
NextToken='string'
)
string
[REQUIRED]
The unique name of the domain.
integer
The maximum number of recommender schemas to return in the response. The default value is 100.
string
A token received from a previous ListRecommenderSchemas call to retrieve the next page of results.
dict
Response Syntax
{
'NextToken': 'string',
'RecommenderSchemas': [
{
'RecommenderSchemaName': 'string',
'Fields': {
'string': [
{
'TargetFieldName': 'string',
'ContentType': 'STRING'|'NUMBER',
'FeatureType': 'TEXTUAL'|'CATEGORICAL'
},
]
},
'CreatedAt': datetime(2015, 1, 1),
'Status': 'ACTIVE'|'DELETING'
},
]
}
Response Structure
(dict) --
NextToken (string) --
A token to retrieve the next page of results. Null if there are no more results to retrieve.
RecommenderSchemas (list) --
A list of recommender schemas and their properties in the specified domain.
(dict) --
Provides a summary of a recommender schema's configuration and current state.
RecommenderSchemaName (string) --
The name of the recommender schema.
Fields (dict) --
A map of dataset type to column definitions included in the schema.
(string) --
(list) --
(dict) --
Defines a column in a recommender schema, including the target field name and optional feature and content type settings for training.
TargetFieldName (string) --
The name of the target field in the dataset, such as Location.City or Attributes.MealTime.
ContentType (string) --
The data type of the column value. Valid values are String and Number. The default value is String.
FeatureType (string) --
How the column is treated for model training. Valid values are CATEGORICAL and TEXTUAL.
CreatedAt (datetime) --
The timestamp when the recommender schema was created.
Status (string) --
The current operational status of the recommender schema.
Deletes a recommender schema from a domain.
See also: AWS API Documentation
Request Syntax
client.delete_recommender_schema(
DomainName='string',
RecommenderSchemaName='string'
)
string
[REQUIRED]
The unique name of the domain.
string
[REQUIRED]
The name of the recommender schema to delete.
dict
Response Syntax
{}
Response Structure
(dict) --
{'RecommenderConfig': {'IncludedColumns': {'string': ['string']}},
'RecommenderSchemaName': 'string'}
Creates a recommender
See also: AWS API Documentation
Request Syntax
client.create_recommender(
DomainName='string',
RecommenderName='string',
RecommenderRecipeName='recommended-for-you'|'similar-items'|'frequently-paired-items'|'popular-items'|'trending-now'|'personalized-ranking',
RecommenderConfig={
'EventsConfig': {
'EventParametersList': [
{
'EventType': 'string',
'EventValueThreshold': 123.0,
'EventWeight': 123.0
},
]
},
'TrainingFrequency': 123,
'InferenceConfig': {
'MinProvisionedTPS': 123
},
'IncludedColumns': {
'string': [
'string',
]
}
},
Description='string',
RecommenderSchemaName='string',
Tags={
'string': 'string'
}
)
string
[REQUIRED]
The unique name of the domain.
string
[REQUIRED]
The name of the recommender.
string
[REQUIRED]
The name of the recommeder recipe.
dict
The recommender configuration.
EventsConfig (dict) --
Configuration settings for how the recommender processes and uses events.
EventParametersList (list) -- [REQUIRED]
A list of event parameters configurations that specify how different event types should be handled.
(dict) --
Configuration parameters for events in the personalization system.
EventType (string) -- [REQUIRED]
The type of event being tracked (e.g., 'click', 'purchase', 'view').
EventValueThreshold (float) --
The minimum value threshold that an event must meet to be considered valid.
EventWeight (float) --
The weight of the event type. A higher weight means higher importance of the event type for the created solution.
TrainingFrequency (integer) --
How often the recommender should retrain its model with new data.
InferenceConfig (dict) --
Configuration settings for how the recommender handles inference requests.
MinProvisionedTPS (integer) --
The minimum provisioned transactions per second (TPS) that the recommender supports. The default value is 1. A high MinProvisionedTPS will increase your cost.
IncludedColumns (dict) --
A map of dataset type to a list of column names to train on. The column names must be a subset of the columns defined in the recommender schema. If not specified, all columns in the schema are used for training. The following columns are always included and do not need to be specified: Item.Id, ItemList[].Id, EventTimestamp, EventType, and EventValue.
(string) --
(list) --
(string) --
string
The description of the domain object type.
string
The name of the recommender schema to use for this recommender. If not specified, the default schema is used.
dict
The tags used to organize, track, or control access for this resource.
(string) --
(string) --
dict
Response Syntax
{
'RecommenderArn': 'string',
'Tags': {
'string': 'string'
}
}
Response Structure
(dict) --
RecommenderArn (string) --
The ARN of the recommender
Tags (dict) --
The tags used to organize, track, or control access for this resource.
(string) --
(string) --
{'RecommenderSchemaName': 'string'}
Creates a recommender filter. A recommender filter specifies which items to include or exclude from recommendations.
See also: AWS API Documentation
Request Syntax
client.create_recommender_filter(
DomainName='string',
RecommenderFilterName='string',
RecommenderFilterExpression='string',
RecommenderSchemaName='string',
Description='string',
Tags={
'string': 'string'
}
)
string
[REQUIRED]
The unique name of the domain.
string
[REQUIRED]
The name of the recommender filter. The name must be unique within the domain.
string
[REQUIRED]
The filter expression that defines which items to include or exclude from recommendations.
string
The name of the recommender schema to use for this recommender filter. If not specified, the default schema is used.
string
A description of the recommender filter.
dict
The tags used to organize, track, or control access for this resource.
(string) --
(string) --
dict
Response Syntax
{
'RecommenderFilterArn': 'string',
'Tags': {
'string': 'string'
}
}
Response Structure
(dict) --
RecommenderFilterArn (string) --
The Amazon Resource Name (ARN) of the recommender filter.
Tags (dict) --
The tags used to organize, track, or control access for this resource.
(string) --
(string) --
{'LatestRecommenderUpdate': {'RecommenderConfig': {'IncludedColumns': {'string': ['string']}}},
'RecommenderConfig': {'IncludedColumns': {'string': ['string']}},
'RecommenderSchemaName': 'string'}
Retrieves a recommender.
See also: AWS API Documentation
Request Syntax
client.get_recommender(
DomainName='string',
RecommenderName='string',
TrainingMetricsCount=123
)
string
[REQUIRED]
The unique name of the domain.
string
[REQUIRED]
The name of the recommender.
integer
The number of training metrics to retrieve for the recommender.
dict
Response Syntax
{
'RecommenderName': 'string',
'RecommenderRecipeName': 'recommended-for-you'|'similar-items'|'frequently-paired-items'|'popular-items'|'trending-now'|'personalized-ranking',
'RecommenderSchemaName': 'string',
'RecommenderConfig': {
'EventsConfig': {
'EventParametersList': [
{
'EventType': 'string',
'EventValueThreshold': 123.0,
'EventWeight': 123.0
},
]
},
'TrainingFrequency': 123,
'InferenceConfig': {
'MinProvisionedTPS': 123
},
'IncludedColumns': {
'string': [
'string',
]
}
},
'Description': 'string',
'Status': 'PENDING'|'IN_PROGRESS'|'ACTIVE'|'FAILED'|'STOPPING'|'INACTIVE'|'STARTING'|'DELETING',
'LastUpdatedAt': datetime(2015, 1, 1),
'CreatedAt': datetime(2015, 1, 1),
'FailureReason': 'string',
'LatestRecommenderUpdate': {
'RecommenderConfig': {
'EventsConfig': {
'EventParametersList': [
{
'EventType': 'string',
'EventValueThreshold': 123.0,
'EventWeight': 123.0
},
]
},
'TrainingFrequency': 123,
'InferenceConfig': {
'MinProvisionedTPS': 123
},
'IncludedColumns': {
'string': [
'string',
]
}
},
'Status': 'PENDING'|'IN_PROGRESS'|'ACTIVE'|'FAILED'|'STOPPING'|'INACTIVE'|'STARTING'|'DELETING',
'CreatedAt': datetime(2015, 1, 1),
'LastUpdatedAt': datetime(2015, 1, 1),
'FailureReason': 'string'
},
'TrainingMetrics': [
{
'Time': datetime(2015, 1, 1),
'Metrics': {
'string': 123.0
}
},
],
'Tags': {
'string': 'string'
}
}
Response Structure
(dict) --
RecommenderName (string) --
The name of the recommender.
RecommenderRecipeName (string) --
The name of the recipe used by the recommender to generate recommendations.
RecommenderSchemaName (string) --
The name of the recommender schema associated with this recommender.
RecommenderConfig (dict) --
The configuration settings for the recommender, including parameters and settings that define its behavior.
EventsConfig (dict) --
Configuration settings for how the recommender processes and uses events.
EventParametersList (list) --
A list of event parameters configurations that specify how different event types should be handled.
(dict) --
Configuration parameters for events in the personalization system.
EventType (string) --
The type of event being tracked (e.g., 'click', 'purchase', 'view').
EventValueThreshold (float) --
The minimum value threshold that an event must meet to be considered valid.
EventWeight (float) --
The weight of the event type. A higher weight means higher importance of the event type for the created solution.
TrainingFrequency (integer) --
How often the recommender should retrain its model with new data.
InferenceConfig (dict) --
Configuration settings for how the recommender handles inference requests.
MinProvisionedTPS (integer) --
The minimum provisioned transactions per second (TPS) that the recommender supports. The default value is 1. A high MinProvisionedTPS will increase your cost.
IncludedColumns (dict) --
A map of dataset type to a list of column names to train on. The column names must be a subset of the columns defined in the recommender schema. If not specified, all columns in the schema are used for training. The following columns are always included and do not need to be specified: Item.Id, ItemList[].Id, EventTimestamp, EventType, and EventValue.
(string) --
(list) --
(string) --
Description (string) --
A detailed description of the recommender providing information about its purpose and functionality.
Status (string) --
The current status of the recommender, indicating whether it is active, creating, updating, or in another state.
LastUpdatedAt (datetime) --
The timestamp of when the recommender was edited.
CreatedAt (datetime) --
The timestamp of when the recommender was created.
FailureReason (string) --
If the recommender fails, provides the reason for the failure.
LatestRecommenderUpdate (dict) --
Information about the most recent update performed on the recommender, including status and timestamp.
RecommenderConfig (dict) --
The updated configuration settings applied to the recommender during this update.
EventsConfig (dict) --
Configuration settings for how the recommender processes and uses events.
EventParametersList (list) --
A list of event parameters configurations that specify how different event types should be handled.
(dict) --
Configuration parameters for events in the personalization system.
EventType (string) --
The type of event being tracked (e.g., 'click', 'purchase', 'view').
EventValueThreshold (float) --
The minimum value threshold that an event must meet to be considered valid.
EventWeight (float) --
The weight of the event type. A higher weight means higher importance of the event type for the created solution.
TrainingFrequency (integer) --
How often the recommender should retrain its model with new data.
InferenceConfig (dict) --
Configuration settings for how the recommender handles inference requests.
MinProvisionedTPS (integer) --
The minimum provisioned transactions per second (TPS) that the recommender supports. The default value is 1. A high MinProvisionedTPS will increase your cost.
IncludedColumns (dict) --
A map of dataset type to a list of column names to train on. The column names must be a subset of the columns defined in the recommender schema. If not specified, all columns in the schema are used for training. The following columns are always included and do not need to be specified: Item.Id, ItemList[].Id, EventTimestamp, EventType, and EventValue.
(string) --
(list) --
(string) --
Status (string) --
The current status of the recommender update operation.
CreatedAt (datetime) --
The timestamp when this recommender update was initiated.
LastUpdatedAt (datetime) --
The timestamp of when the recommender was edited.
FailureReason (string) --
If the update operation failed, provides the reason for the failure.
TrainingMetrics (list) --
A set of metrics that provide information about the recommender's training performance and accuracy.
(dict) --
Contains metrics and performance indicators from the training of a recommender model.
Time (datetime) --
The timestamp when these training metrics were recorded.
Metrics (dict) --
A collection of performance metrics and statistics from the training process.
(string) --
(float) --
Tags (dict) --
The tags used to organize, track, or control access for this resource.
(string) --
(string) --
{'RecommenderSchemaName': 'string'}
Retrieves information about a specific recommender filter in a domain.
See also: AWS API Documentation
Request Syntax
client.get_recommender_filter(
DomainName='string',
RecommenderFilterName='string'
)
string
[REQUIRED]
The unique name of the domain.
string
[REQUIRED]
The name of the recommender filter to retrieve.
dict
Response Syntax
{
'RecommenderFilterName': 'string',
'RecommenderFilterExpression': 'string',
'RecommenderSchemaName': 'string',
'CreatedAt': datetime(2015, 1, 1),
'Status': 'ACTIVE'|'PENDING'|'IN_PROGRESS'|'FAILED'|'DELETING',
'Description': 'string',
'FailureReason': 'string',
'Tags': {
'string': 'string'
}
}
Response Structure
(dict) --
RecommenderFilterName (string) --
The name of the recommender filter.
RecommenderFilterExpression (string) --
The filter expression that defines which items to include or exclude from recommendations.
RecommenderSchemaName (string) --
The name of the recommender schema associated with this recommender filter.
CreatedAt (datetime) --
The timestamp of when the recommender filter was created.
Status (string) --
The status of the recommender filter.
Description (string) --
The description of the recommender filter.
FailureReason (string) --
If the recommender filter failed, provides the reason for the failure.
Tags (dict) --
The tags used to organize, track, or control access for this resource.
(string) --
(string) --
{'RecommenderFilters': {'RecommenderSchemaName': 'string'}}
Returns a list of recommender filters in the specified domain.
See also: AWS API Documentation
Request Syntax
client.list_recommender_filters(
DomainName='string',
MaxResults=123,
NextToken='string'
)
string
[REQUIRED]
The unique name of the domain.
integer
The maximum number of recommender filters to return in the response. The default value is 100.
string
A token received from a previous ListRecommenderFilters call to retrieve the next page of results.
dict
Response Syntax
{
'NextToken': 'string',
'RecommenderFilters': [
{
'RecommenderFilterName': 'string',
'RecommenderSchemaName': 'string',
'RecommenderFilterExpression': 'string',
'CreatedAt': datetime(2015, 1, 1),
'Description': 'string',
'Status': 'ACTIVE'|'PENDING'|'IN_PROGRESS'|'FAILED'|'DELETING',
'FailureReason': 'string',
'Tags': {
'string': 'string'
}
},
]
}
Response Structure
(dict) --
NextToken (string) --
A token to retrieve the next page of results. Null if there are no more results to retrieve.
RecommenderFilters (list) --
A list of recommender filters and their properties in the specified domain.
(dict) --
Provides a summary of a recommender filter's configuration and current state.
RecommenderFilterName (string) --
The name of the recommender filter.
RecommenderSchemaName (string) --
The name of the recommender schema associated with this recommender filter.
RecommenderFilterExpression (string) --
The filter expression that defines which items to include or exclude from recommendations.
CreatedAt (datetime) --
The timestamp when the recommender filter was created.
Description (string) --
A description of the recommender filter's purpose and characteristics.
Status (string) --
The current operational status of the recommender filter.
FailureReason (string) --
If the recommender filter is in a failed state, provides the reason for the failure.
Tags (dict) --
The tags used to organize, track, or control access for this resource.
(string) --
(string) --
{'Recommenders': {'LatestRecommenderUpdate': {'RecommenderConfig': {'IncludedColumns': {'string': ['string']}}},
'RecommenderConfig': {'IncludedColumns': {'string': ['string']}},
'RecommenderSchemaName': 'string'}}
Returns a list of recommenders in the specified domain.
See also: AWS API Documentation
Request Syntax
client.list_recommenders(
DomainName='string',
MaxResults=123,
NextToken='string'
)
string
[REQUIRED]
The unique name of the domain.
integer
The maximum number of recommenders to return in the response. The default value is 100.
string
A token received from a previous ListRecommenders call to retrieve the next page of results.
dict
Response Syntax
{
'NextToken': 'string',
'Recommenders': [
{
'RecommenderName': 'string',
'RecipeName': 'recommended-for-you'|'similar-items'|'frequently-paired-items'|'popular-items'|'trending-now'|'personalized-ranking',
'RecommenderSchemaName': 'string',
'RecommenderConfig': {
'EventsConfig': {
'EventParametersList': [
{
'EventType': 'string',
'EventValueThreshold': 123.0,
'EventWeight': 123.0
},
]
},
'TrainingFrequency': 123,
'InferenceConfig': {
'MinProvisionedTPS': 123
},
'IncludedColumns': {
'string': [
'string',
]
}
},
'CreatedAt': datetime(2015, 1, 1),
'Description': 'string',
'Status': 'PENDING'|'IN_PROGRESS'|'ACTIVE'|'FAILED'|'STOPPING'|'INACTIVE'|'STARTING'|'DELETING',
'LastUpdatedAt': datetime(2015, 1, 1),
'Tags': {
'string': 'string'
},
'FailureReason': 'string',
'LatestRecommenderUpdate': {
'RecommenderConfig': {
'EventsConfig': {
'EventParametersList': [
{
'EventType': 'string',
'EventValueThreshold': 123.0,
'EventWeight': 123.0
},
]
},
'TrainingFrequency': 123,
'InferenceConfig': {
'MinProvisionedTPS': 123
},
'IncludedColumns': {
'string': [
'string',
]
}
},
'Status': 'PENDING'|'IN_PROGRESS'|'ACTIVE'|'FAILED'|'STOPPING'|'INACTIVE'|'STARTING'|'DELETING',
'CreatedAt': datetime(2015, 1, 1),
'LastUpdatedAt': datetime(2015, 1, 1),
'FailureReason': 'string'
}
},
]
}
Response Structure
(dict) --
NextToken (string) --
A token to retrieve the next page of results. Null if there are no more results to retrieve.
Recommenders (list) --
A list of recommenders and their properties in the specified domain.
(dict) --
Provides a summary of a recommender's configuration and current state.
RecommenderName (string) --
The name of the recommender.
RecipeName (string) --
The name of the recipe used by this recommender.
RecommenderSchemaName (string) --
The name of the recommender schema associated with this recommender.
RecommenderConfig (dict) --
The configuration settings applied to this recommender.
EventsConfig (dict) --
Configuration settings for how the recommender processes and uses events.
EventParametersList (list) --
A list of event parameters configurations that specify how different event types should be handled.
(dict) --
Configuration parameters for events in the personalization system.
EventType (string) --
The type of event being tracked (e.g., 'click', 'purchase', 'view').
EventValueThreshold (float) --
The minimum value threshold that an event must meet to be considered valid.
EventWeight (float) --
The weight of the event type. A higher weight means higher importance of the event type for the created solution.
TrainingFrequency (integer) --
How often the recommender should retrain its model with new data.
InferenceConfig (dict) --
Configuration settings for how the recommender handles inference requests.
MinProvisionedTPS (integer) --
The minimum provisioned transactions per second (TPS) that the recommender supports. The default value is 1. A high MinProvisionedTPS will increase your cost.
IncludedColumns (dict) --
A map of dataset type to a list of column names to train on. The column names must be a subset of the columns defined in the recommender schema. If not specified, all columns in the schema are used for training. The following columns are always included and do not need to be specified: Item.Id, ItemList[].Id, EventTimestamp, EventType, and EventValue.
(string) --
(list) --
(string) --
CreatedAt (datetime) --
The timestamp when the recommender was created.
Description (string) --
A description of the recommender's purpose and characteristics.
Status (string) --
The current operational status of the recommender.
LastUpdatedAt (datetime) --
The timestamp of when the recommender was edited.
Tags (dict) --
The tags used to organize, track, or control access for this resource.
(string) --
(string) --
FailureReason (string) --
If the recommender is in a failed state, provides the reason for the failure.
LatestRecommenderUpdate (dict) --
Information about the most recent update performed on the recommender, including its status and timing.
RecommenderConfig (dict) --
The updated configuration settings applied to the recommender during this update.
EventsConfig (dict) --
Configuration settings for how the recommender processes and uses events.
EventParametersList (list) --
A list of event parameters configurations that specify how different event types should be handled.
(dict) --
Configuration parameters for events in the personalization system.
EventType (string) --
The type of event being tracked (e.g., 'click', 'purchase', 'view').
EventValueThreshold (float) --
The minimum value threshold that an event must meet to be considered valid.
EventWeight (float) --
The weight of the event type. A higher weight means higher importance of the event type for the created solution.
TrainingFrequency (integer) --
How often the recommender should retrain its model with new data.
InferenceConfig (dict) --
Configuration settings for how the recommender handles inference requests.
MinProvisionedTPS (integer) --
The minimum provisioned transactions per second (TPS) that the recommender supports. The default value is 1. A high MinProvisionedTPS will increase your cost.
IncludedColumns (dict) --
A map of dataset type to a list of column names to train on. The column names must be a subset of the columns defined in the recommender schema. If not specified, all columns in the schema are used for training. The following columns are always included and do not need to be specified: Item.Id, ItemList[].Id, EventTimestamp, EventType, and EventValue.
(string) --
(list) --
(string) --
Status (string) --
The current status of the recommender update operation.
CreatedAt (datetime) --
The timestamp when this recommender update was initiated.
LastUpdatedAt (datetime) --
The timestamp of when the recommender was edited.
FailureReason (string) --
If the update operation failed, provides the reason for the failure.
{'RecommenderConfig': {'IncludedColumns': {'string': ['string']}}}
Updates the properties of an existing recommender, allowing you to modify its configuration and description.
See also: AWS API Documentation
Request Syntax
client.update_recommender(
DomainName='string',
RecommenderName='string',
Description='string',
RecommenderConfig={
'EventsConfig': {
'EventParametersList': [
{
'EventType': 'string',
'EventValueThreshold': 123.0,
'EventWeight': 123.0
},
]
},
'TrainingFrequency': 123,
'InferenceConfig': {
'MinProvisionedTPS': 123
},
'IncludedColumns': {
'string': [
'string',
]
}
}
)
string
[REQUIRED]
The unique name of the domain.
string
[REQUIRED]
The name of the recommender to update.
string
The new description to assign to the recommender.
dict
The new configuration settings to apply to the recommender, including updated parameters and settings that define its behavior.
EventsConfig (dict) --
Configuration settings for how the recommender processes and uses events.
EventParametersList (list) -- [REQUIRED]
A list of event parameters configurations that specify how different event types should be handled.
(dict) --
Configuration parameters for events in the personalization system.
EventType (string) -- [REQUIRED]
The type of event being tracked (e.g., 'click', 'purchase', 'view').
EventValueThreshold (float) --
The minimum value threshold that an event must meet to be considered valid.
EventWeight (float) --
The weight of the event type. A higher weight means higher importance of the event type for the created solution.
TrainingFrequency (integer) --
How often the recommender should retrain its model with new data.
InferenceConfig (dict) --
Configuration settings for how the recommender handles inference requests.
MinProvisionedTPS (integer) --
The minimum provisioned transactions per second (TPS) that the recommender supports. The default value is 1. A high MinProvisionedTPS will increase your cost.
IncludedColumns (dict) --
A map of dataset type to a list of column names to train on. The column names must be a subset of the columns defined in the recommender schema. If not specified, all columns in the schema are used for training. The following columns are always included and do not need to be specified: Item.Id, ItemList[].Id, EventTimestamp, EventType, and EventValue.
(string) --
(list) --
(string) --
dict
Response Syntax
{
'RecommenderName': 'string'
}
Response Structure
(dict) --
RecommenderName (string) --
The name of the recommender that was updated.