View source: R/machinelearning_service.R
machinelearning | R Documentation |
Definition of the public APIs exposed by Amazon Machine Learning
machinelearning(
config = list(),
credentials = list(),
endpoint = NULL,
region = NULL
)
config |
Optional configuration of credentials, endpoint, and/or region.
|
credentials |
Optional credentials shorthand for the config parameter
|
endpoint |
Optional shorthand for complete URL to use for the constructed client. |
region |
Optional shorthand for AWS Region used in instantiating the client. |
A client for the service. You can call the service's operations using
syntax like svc$operation(...)
, where svc
is the name you've assigned
to the client. The available operations are listed in the
Operations section.
svc <- machinelearning( config = list( credentials = list( creds = list( access_key_id = "string", secret_access_key = "string", session_token = "string" ), profile = "string", anonymous = "logical" ), endpoint = "string", region = "string", close_connection = "logical", timeout = "numeric", s3_force_path_style = "logical", sts_regional_endpoint = "string" ), credentials = list( creds = list( access_key_id = "string", secret_access_key = "string", session_token = "string" ), profile = "string", anonymous = "logical" ), endpoint = "string", region = "string" )
add_tags | Adds one or more tags to an object, up to a limit of 10 |
create_batch_prediction | Generates predictions for a group of observations |
create_data_source_from_rds | Creates a DataSource object from an Amazon Relational Database Service (Amazon RDS) |
create_data_source_from_redshift | Creates a DataSource from a database hosted on an Amazon Redshift cluster |
create_data_source_from_s3 | Creates a DataSource object |
create_evaluation | Creates a new Evaluation of an MLModel |
create_ml_model | Creates a new MLModel using the DataSource and the recipe as information sources |
create_realtime_endpoint | Creates a real-time endpoint for the MLModel |
delete_batch_prediction | Assigns the DELETED status to a BatchPrediction, rendering it unusable |
delete_data_source | Assigns the DELETED status to a DataSource, rendering it unusable |
delete_evaluation | Assigns the DELETED status to an Evaluation, rendering it unusable |
delete_ml_model | Assigns the DELETED status to an MLModel, rendering it unusable |
delete_realtime_endpoint | Deletes a real time endpoint of an MLModel |
delete_tags | Deletes the specified tags associated with an ML object |
describe_batch_predictions | Returns a list of BatchPrediction operations that match the search criteria in the request |
describe_data_sources | Returns a list of DataSource that match the search criteria in the request |
describe_evaluations | Returns a list of DescribeEvaluations that match the search criteria in the request |
describe_ml_models | Returns a list of MLModel that match the search criteria in the request |
describe_tags | Describes one or more of the tags for your Amazon ML object |
get_batch_prediction | Returns a BatchPrediction that includes detailed metadata, status, and data file information for a Batch Prediction request |
get_data_source | Returns a DataSource that includes metadata and data file information, as well as the current status of the DataSource |
get_evaluation | Returns an Evaluation that includes metadata as well as the current status of the Evaluation |
get_ml_model | Returns an MLModel that includes detailed metadata, data source information, and the current status of the MLModel |
predict | Generates a prediction for the observation using the specified ML Model |
update_batch_prediction | Updates the BatchPredictionName of a BatchPrediction |
update_data_source | Updates the DataSourceName of a DataSource |
update_evaluation | Updates the EvaluationName of an Evaluation |
update_ml_model | Updates the MLModelName and the ScoreThreshold of an MLModel |
## Not run:
svc <- machinelearning()
svc$add_tags(
Foo = 123
)
## End(Not run)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.