ExAws.MachineLearning.Core
Amazon Machine Learning
Definition of the public APIs exposed by Amazon Machine Learning
Summary↑
Types ↑
realtime_endpoint_status :: binary
rds_metadata :: [data_pipeline_id: edp_pipeline_id, database: rds_database, database_user_name: rds_database_username, resource_role: edp_resource_role, select_sql_query: rds_select_sql_query, service_role: edp_service_role]
update_batch_prediction_input :: [batch_prediction_id: entity_id, batch_prediction_name: entity_name]
delete_realtime_endpoint_input :: [{:ml_model_id, entity_id}]
recipe :: binary
float_label :: float
get_batch_prediction_output :: [batch_prediction_data_source_id: entity_id, batch_prediction_id: entity_id, created_at: epoch_time, created_by_iam_user: aws_user_arn, input_data_location_s3: s3_url, last_updated_at: epoch_time, log_uri: presigned_s3_url, ml_model_id: entity_id, message: message, name: entity_name, output_uri: s3_url, status: entity_status]
entity_id :: binary
get_ml_model_output :: [created_at: epoch_time, created_by_iam_user: aws_user_arn, endpoint_info: realtime_endpoint_info, input_data_location_s3: s3_url, last_updated_at: epoch_time, log_uri: presigned_s3_url, ml_model_id: entity_id, ml_model_type: ml_model_type, message: message, name: ml_model_name, recipe: recipe, schema: data_schema, score_threshold: score_threshold, score_threshold_last_updated_at: epoch_time, size_in_bytes: long_type, status: entity_status, training_data_source_id: entity_id, training_parameters: training_parameters]
redshift_database_username :: binary
redshift_metadata :: [database_user_name: redshift_database_username, redshift_database: redshift_database, select_sql_query: redshift_select_sql_query]
delete_realtime_endpoint_output :: [ml_model_id: entity_id, realtime_endpoint_info: realtime_endpoint_info]
edp_resource_role :: binary
create_batch_prediction_input :: [batch_prediction_data_source_id: entity_id, batch_prediction_id: entity_id, batch_prediction_name: entity_name, ml_model_id: entity_id, output_uri: s3_url]
get_data_source_output :: [compute_statistics: compute_statistics, created_at: epoch_time, created_by_iam_user: aws_user_arn, data_location_s3: s3_url, data_rearrangement: data_rearrangement, data_size_in_bytes: long_type, data_source_id: entity_id, data_source_schema: data_schema, last_updated_at: epoch_time, log_uri: presigned_s3_url, message: message, name: entity_name, number_of_files: long_type, rds_metadata: rds_metadata, redshift_metadata: redshift_metadata, role_arn: role_arn, status: entity_status]
update_evaluation_input :: [evaluation_id: entity_id, evaluation_name: entity_name]
redshift_cluster_identifier :: binary
s3_data_spec :: [data_location_s3: s3_url, data_rearrangement: data_rearrangement, data_schema: data_schema, data_schema_location_s3: s3_url]
error_message :: binary
redshift_database_name :: binary
rds_database_credentials :: [password: rds_database_password, username: rds_database_username]
get_evaluation_output :: [created_at: epoch_time, created_by_iam_user: aws_user_arn, evaluation_data_source_id: entity_id, evaluation_id: entity_id, input_data_location_s3: s3_url, last_updated_at: epoch_time, log_uri: presigned_s3_url, ml_model_id: entity_id, message: message, name: entity_name, performance_metrics: performance_metrics, status: entity_status]
ml_model_type :: binary
aws_user_arn :: binary
ml_model_filter_variable :: binary
create_realtime_endpoint_input :: [{:ml_model_id, entity_id}]
sort_order :: binary
details_attributes :: binary
entity_status :: binary
predict_output :: [{:prediction, prediction}]
update_ml_model_input :: [ml_model_id: entity_id, ml_model_name: entity_name, score_threshold: score_threshold]
create_data_source_from_rds_output :: [{:data_source_id, entity_id}]
delete_batch_prediction_input :: [{:batch_prediction_id, entity_id}]
delete_batch_prediction_output :: [{:batch_prediction_id, entity_id}]
score_value :: float
performance_metrics_property_key :: binary
performance_metrics_property_value :: binary
variable_value :: binary
describe_batch_predictions_input :: [eq: comparator_value, filter_variable: batch_prediction_filter_variable, ge: comparator_value, gt: comparator_value, le: comparator_value, lt: comparator_value, limit: page_limit, ne: comparator_value, next_token: string_type, prefix: comparator_value, sort_order: sort_order]
rds_data_spec :: [data_rearrangement: data_rearrangement, data_schema: data_schema, data_schema_uri: s3_url, database_credentials: rds_database_credentials, database_information: rds_database, resource_role: edp_resource_role, s3_staging_location: s3_url, security_group_ids: edp_security_group_ids, select_sql_query: rds_select_sql_query, service_role: edp_service_role, subnet_id: edp_subnet_id]
create_realtime_endpoint_output :: [ml_model_id: entity_id, realtime_endpoint_info: realtime_endpoint_info]
edp_service_role :: binary
rds_database_password :: binary
create_evaluation_input :: [evaluation_data_source_id: entity_id, evaluation_id: entity_id, evaluation_name: entity_name, ml_model_id: entity_id]
role_arn :: binary
data_schema :: binary
prediction :: [details: details_map, predicted_label: label, predicted_scores: score_value_per_label_map, predicted_value: float_label]
details_value :: binary
performance_metrics_properties :: [{performance_metrics_property_key, performance_metrics_property_value}]
update_data_source_output :: [{:data_source_id, entity_id}]
error_code :: integer
delete_data_source_output :: [{:data_source_id, entity_id}]
message :: binary
describe_evaluations_output :: [next_token: string_type, results: evaluations]
evaluation :: [created_at: epoch_time, created_by_iam_user: aws_user_arn, evaluation_data_source_id: entity_id, evaluation_id: entity_id, input_data_location_s3: s3_url, last_updated_at: epoch_time, ml_model_id: entity_id, message: message, name: entity_name, performance_metrics: performance_metrics, status: entity_status]
update_evaluation_output :: [{:evaluation_id, entity_id}]
integer_type :: integer
presigned_s3_url :: binary
edp_subnet_id :: binary
idempotent_parameter_mismatch_exception :: [code: error_code, message: error_message]
delete_data_source_input :: [{:data_source_id, entity_id}]
predict_input :: [ml_model_id: entity_id, predict_endpoint: vip_url, record: machine_learning_record]
create_data_source_from_s3_output :: [{:data_source_id, entity_id}]
vip_url :: binary
entity_name :: binary
describe_evaluations_input :: [eq: comparator_value, filter_variable: evaluation_filter_variable, ge: comparator_value, gt: comparator_value, le: comparator_value, lt: comparator_value, limit: page_limit, ne: comparator_value, next_token: string_type, prefix: comparator_value, sort_order: sort_order]
redshift_select_sql_query :: binary
ml_model_name :: binary
rds_database_name :: binary
edp_pipeline_id :: binary
create_data_source_from_redshift_output :: [{:data_source_id, entity_id}]
batch_prediction :: [batch_prediction_data_source_id: entity_id, batch_prediction_id: entity_id, created_at: epoch_time, created_by_iam_user: aws_user_arn, input_data_location_s3: s3_url, last_updated_at: epoch_time, ml_model_id: entity_id, message: message, name: entity_name, output_uri: s3_url, status: entity_status]
describe_ml_models_input :: [eq: comparator_value, filter_variable: ml_model_filter_variable, ge: comparator_value, gt: comparator_value, le: comparator_value, lt: comparator_value, limit: page_limit, ne: comparator_value, next_token: string_type, prefix: comparator_value, sort_order: sort_order]
performance_metrics :: [{:properties, performance_metrics_properties}]
create_ml_model_output :: [{:ml_model_id, entity_id}]
limit_exceeded_exception :: [code: error_code, message: error_message]
rds_database :: [database_name: rds_database_name, instance_identifier: rds_instance_identifier]
delete_evaluation_output :: [{:evaluation_id, entity_id}]
redshift_data_spec :: [data_rearrangement: data_rearrangement, data_schema: data_schema, data_schema_uri: s3_url, database_credentials: redshift_database_credentials, database_information: redshift_database, s3_staging_location: s3_url, select_sql_query: redshift_select_sql_query]
data_rearrangement :: binary
resource_not_found_exception :: [code: error_code, message: error_message]
rds_select_sql_query :: binary
predictor_not_mounted_exception :: [{:message, error_message}]
delete_ml_model_input :: [{:ml_model_id, entity_id}]
get_ml_model_input :: [ml_model_id: entity_id, verbose: verbose]
comparator_value :: binary
get_batch_prediction_input :: [{:batch_prediction_id, entity_id}]
algorithm :: binary
epoch_time :: integer
create_data_source_from_s3_input :: [compute_statistics: compute_statistics, data_source_id: entity_id, data_source_name: entity_name, data_spec: s3_data_spec]
data_source_filter_variable :: binary
update_ml_model_output :: [{:ml_model_id, entity_id}]
ml_model :: [algorithm: algorithm, created_at: epoch_time, created_by_iam_user: aws_user_arn, endpoint_info: realtime_endpoint_info, input_data_location_s3: s3_url, last_updated_at: epoch_time, ml_model_id: entity_id, ml_model_type: ml_model_type, message: message, name: ml_model_name, score_threshold: score_threshold, score_threshold_last_updated_at: epoch_time, size_in_bytes: long_type, status: entity_status, training_data_source_id: entity_id, training_parameters: training_parameters]
redshift_database_password :: binary
create_evaluation_output :: [{:evaluation_id, entity_id}]
describe_batch_predictions_output :: [next_token: string_type, results: batch_predictions]
get_evaluation_input :: [{:evaluation_id, entity_id}]
create_data_source_from_rds_input :: [compute_statistics: compute_statistics, data_source_id: entity_id, data_source_name: entity_name, rds_data: rds_data_spec, role_arn: role_arn]
update_data_source_input :: [data_source_id: entity_id, data_source_name: entity_name]
redshift_database_credentials :: [password: redshift_database_password, username: redshift_database_username]
rds_database_username :: binary
delete_ml_model_output :: [{:ml_model_id, entity_id}]
realtime_endpoint_info :: [created_at: epoch_time, endpoint_status: realtime_endpoint_status, endpoint_url: vip_url, peak_requests_per_second: integer_type]
describe_ml_models_output :: [next_token: string_type, results: ml_models]
update_batch_prediction_output :: [{:batch_prediction_id, entity_id}]
evaluation_filter_variable :: binary
score_threshold :: float
compute_statistics :: boolean
create_batch_prediction_output :: [{:batch_prediction_id, entity_id}]
internal_server_exception :: [code: error_code, message: error_message]
redshift_database :: [cluster_identifier: redshift_cluster_identifier, database_name: redshift_database_name]
edp_security_group_id :: binary
batch_prediction_filter_variable :: binary
delete_evaluation_input :: [{:evaluation_id, entity_id}]
describe_data_sources_input :: [eq: comparator_value, filter_variable: data_source_filter_variable, ge: comparator_value, gt: comparator_value, le: comparator_value, lt: comparator_value, limit: page_limit, ne: comparator_value, next_token: string_type, prefix: comparator_value, sort_order: sort_order]
label :: binary
verbose :: boolean
data_sources :: [data_source]
long_type :: integer
get_data_source_input :: [data_source_id: entity_id, verbose: verbose]
variable_name :: binary
rds_instance_identifier :: binary
describe_data_sources_output :: [next_token: string_type, results: data_sources]
evaluations :: [evaluation]
page_limit :: integer
s3_url :: binary
create_ml_model_input :: [ml_model_id: entity_id, ml_model_name: entity_name, ml_model_type: ml_model_type, parameters: training_parameters, recipe: recipe, recipe_uri: s3_url, training_data_source_id: entity_id]
data_source :: [compute_statistics: compute_statistics, created_at: epoch_time, created_by_iam_user: aws_user_arn, data_location_s3: s3_url, data_rearrangement: data_rearrangement, data_size_in_bytes: long_type, data_source_id: entity_id, last_updated_at: epoch_time, message: message, name: entity_name, number_of_files: long_type, rds_metadata: rds_metadata, redshift_metadata: redshift_metadata, role_arn: role_arn, status: entity_status]
string_type :: binary
create_data_source_from_redshift_input :: [compute_statistics: compute_statistics, data_source_id: entity_id, data_source_name: entity_name, data_spec: redshift_data_spec, role_arn: role_arn]
invalid_input_exception :: [code: error_code, message: error_message]
Functions
Specs:
- create_batch_prediction(client :: ExAws.MachineLearning.t, input :: create_batch_prediction_input) :: ExAws.Request.JSON.response_t
CreateBatchPrediction
Generates predictions for a group of observations. The observations to
process exist in one or more data files referenced by a DataSource
. This
operation creates a new BatchPrediction
, and uses an MLModel
and the
data files referenced by the DataSource
as information sources.
CreateBatchPrediction
is an asynchronous operation. In response to
CreateBatchPrediction
, Amazon Machine Learning (Amazon ML) immediately
returns and sets the BatchPrediction
status to PENDING
. After the
BatchPrediction
completes, Amazon ML sets the status to COMPLETED
.
You can poll for status updates by using the GetBatchPrediction
operation
and checking the Status
parameter of the result. After the COMPLETED
status appears, the results are available in the location specified by the
OutputUri
parameter.
Specs:
- create_batch_prediction!(client :: ExAws.MachineLearning.t, input :: create_batch_prediction_input) :: ExAws.Request.JSON.success_t | no_return
Same as create_batch_prediction/2
but raise on error.
Specs:
- create_data_source_from_rds(client :: ExAws.MachineLearning.t, input :: create_data_source_from_rds_input) :: ExAws.Request.JSON.response_t
CreateDataSourceFromRDS
Creates a DataSource
object from an Amazon Relational Database
Service (Amazon RDS). A DataSource
references data that can be used to perform CreateMLModel
,
CreateEvaluation
, or CreateBatchPrediction
operations.
CreateDataSourceFromRDS
is an asynchronous operation. In response to
CreateDataSourceFromRDS
, Amazon Machine Learning (Amazon ML) immediately
returns and sets the DataSource
status to PENDING
. After the
DataSource
is created and ready for use, Amazon ML sets the Status
parameter to COMPLETED
. DataSource
in COMPLETED
or PENDING
status
can only be used to perform CreateMLModel
, CreateEvaluation
, or
CreateBatchPrediction
operations.
If Amazon ML cannot accept the input source, it sets the Status
parameter
to FAILED
and includes an error message in the Message
attribute of the
GetDataSource
operation response.
Specs:
- create_data_source_from_rds!(client :: ExAws.MachineLearning.t, input :: create_data_source_from_rds_input) :: ExAws.Request.JSON.success_t | no_return
Same as create_data_source_from_rds/2
but raise on error.
Specs:
- create_data_source_from_redshift(client :: ExAws.MachineLearning.t, input :: create_data_source_from_redshift_input) :: ExAws.Request.JSON.response_t
CreateDataSourceFromRedshift
Creates a DataSource
from Amazon
Redshift. A DataSource
references data
that can be used to perform either CreateMLModel
, CreateEvaluation
or
CreateBatchPrediction
operations.
CreateDataSourceFromRedshift
is an asynchronous operation. In response to
CreateDataSourceFromRedshift
, Amazon Machine Learning (Amazon ML)
immediately returns and sets the DataSource
status to PENDING
. After
the DataSource
is created and ready for use, Amazon ML sets the Status
parameter to COMPLETED
. DataSource
in COMPLETED
or PENDING
status
can only be used to perform CreateMLModel
, CreateEvaluation
, or
CreateBatchPrediction
operations.
If Amazon ML cannot accept the input source, it sets the Status
parameter
to FAILED
and includes an error message in the Message
attribute of the
GetDataSource
operation response.
The observations should exist in the database hosted on an Amazon Redshift
cluster and should be specified by a SelectSqlQuery
. Amazon ML executes
Unload
command in Amazon Redshift to transfer the result set of SelectSqlQuery
to S3StagingLocation.
After the DataSource
is created, it’s ready for use in evaluations and
batch predictions. If you plan to use the DataSource
to train an
MLModel
, the DataSource
requires another item — a recipe. A recipe
describes the observation variables that participate in training an
MLModel
. A recipe describes how each input variable will be used in
training. Will the variable be included or excluded from training? Will the
variable be manipulated, for example, combined with another variable or
split apart into word combinations? The recipe provides answers to these
questions. For more information, see the Amazon Machine Learning Developer
Guide.
Specs:
- create_data_source_from_redshift!(client :: ExAws.MachineLearning.t, input :: create_data_source_from_redshift_input) :: ExAws.Request.JSON.success_t | no_return
Same as create_data_source_from_redshift/2
but raise on error.
Specs:
- create_data_source_from_s3(client :: ExAws.MachineLearning.t, input :: create_data_source_from_s3_input) :: ExAws.Request.JSON.response_t
CreateDataSourceFromS3
Creates a DataSource
object. A DataSource
references data that can be
used to perform CreateMLModel
, CreateEvaluation
, or
CreateBatchPrediction
operations.
CreateDataSourceFromS3
is an asynchronous operation. In response to
CreateDataSourceFromS3
, Amazon Machine Learning (Amazon ML) immediately
returns and sets the DataSource
status to PENDING
. After the
DataSource
is created and ready for use, Amazon ML sets the Status
parameter to COMPLETED
. DataSource
in COMPLETED
or PENDING
status
can only be used to perform CreateMLModel
, CreateEvaluation
or
CreateBatchPrediction
operations.
If Amazon ML cannot accept the input source, it sets the Status
parameter
to FAILED
and includes an error message in the Message
attribute of the
GetDataSource
operation response.
The observation data used in a DataSource
should be ready to use; that
is, it should have a consistent structure, and missing data values should
be kept to a minimum. The observation data must reside in one or more CSV
files in an Amazon Simple Storage Service (Amazon S3) bucket, along with a
schema that describes the data items by name and type. The same schema must
be used for all of the data files referenced by the DataSource
.
After the DataSource
has been created, it’s ready to use in evaluations
and batch predictions. If you plan to use the DataSource
to train an
MLModel
, the DataSource
requires another item: a recipe. A recipe
describes the observation variables that participate in training an
MLModel
. A recipe describes how each input variable will be used in
training. Will the variable be included or excluded from training? Will the
variable be manipulated, for example, combined with another variable, or
split apart into word combinations? The recipe provides answers to these
questions. For more information, see the Amazon Machine Learning Developer
Guide.
Specs:
- create_data_source_from_s3!(client :: ExAws.MachineLearning.t, input :: create_data_source_from_s3_input) :: ExAws.Request.JSON.success_t | no_return
Same as create_data_source_from_s3/2
but raise on error.
Specs:
- create_evaluation(client :: ExAws.MachineLearning.t, input :: create_evaluation_input) :: ExAws.Request.JSON.response_t
CreateEvaluation
Creates a new Evaluation
of an MLModel
. An MLModel
is evaluated on a
set of observations associated to a DataSource
. Like a DataSource
for
an MLModel
, the DataSource
for an Evaluation
contains values for the
Target Variable. The Evaluation
compares the predicted result for each
observation to the actual outcome and provides a summary so that you know
how effective the MLModel
functions on the test data. Evaluation
generates a relevant performance metric such as BinaryAUC, RegressionRMSE
or MulticlassAvgFScore based on the corresponding MLModelType
: BINARY
,
REGRESSION
or MULTICLASS
.
CreateEvaluation
is an asynchronous operation. In response to
CreateEvaluation
, Amazon Machine Learning (Amazon ML) immediately returns
and sets the evaluation status to PENDING
. After the Evaluation
is
created and ready for use, Amazon ML sets the status to COMPLETED
.
You can use the GetEvaluation
operation to check progress of the
evaluation during the creation operation.
Specs:
- create_evaluation!(client :: ExAws.MachineLearning.t, input :: create_evaluation_input) :: ExAws.Request.JSON.success_t | no_return
Same as create_evaluation/2
but raise on error.
Specs:
- create_ml_model(client :: ExAws.MachineLearning.t, input :: create_ml_model_input) :: ExAws.Request.JSON.response_t
CreateMLModel
Creates a new MLModel
using the data files and the recipe as information
sources.
An MLModel
is nearly immutable. Users can only update the MLModelName
and the ScoreThreshold
in an MLModel
without creating a new MLModel
.
CreateMLModel
is an asynchronous operation. In response to
CreateMLModel
, Amazon Machine Learning (Amazon ML) immediately returns
and sets the MLModel
status to PENDING
. After the MLModel
is created
and ready for use, Amazon ML sets the status to COMPLETED
.
You can use the GetMLModel
operation to check progress of the MLModel
during the creation operation.
CreateMLModel
requires a DataSource
with computed statistics, which can
be created by setting ComputeStatistics
to true
in
CreateDataSourceFromRDS
, CreateDataSourceFromS3
, or
CreateDataSourceFromRedshift
operations.
Specs:
- create_ml_model!(client :: ExAws.MachineLearning.t, input :: create_ml_model_input) :: ExAws.Request.JSON.success_t | no_return
Same as create_ml_model/2
but raise on error.
Specs:
- create_realtime_endpoint(client :: ExAws.MachineLearning.t, input :: create_realtime_endpoint_input) :: ExAws.Request.JSON.response_t
CreateRealtimeEndpoint
Creates a real-time endpoint for the MLModel
. The endpoint contains the
URI of the MLModel
; that is, the location to send real-time prediction
requests for the specified MLModel
.
Specs:
- create_realtime_endpoint!(client :: ExAws.MachineLearning.t, input :: create_realtime_endpoint_input) :: ExAws.Request.JSON.success_t | no_return
Same as create_realtime_endpoint/2
but raise on error.
Specs:
- delete_batch_prediction(client :: ExAws.MachineLearning.t, input :: delete_batch_prediction_input) :: ExAws.Request.JSON.response_t
DeleteBatchPrediction
Assigns the DELETED status to a BatchPrediction
, rendering it unusable.
After using the DeleteBatchPrediction
operation, you can use the
GetBatchPrediction
operation to verify that the status of the
BatchPrediction
changed to DELETED.
Specs:
- delete_batch_prediction!(client :: ExAws.MachineLearning.t, input :: delete_batch_prediction_input) :: ExAws.Request.JSON.success_t | no_return
Same as delete_batch_prediction/2
but raise on error.
Specs:
- delete_data_source(client :: ExAws.MachineLearning.t, input :: delete_data_source_input) :: ExAws.Request.JSON.response_t
DeleteDataSource
Assigns the DELETED status to a DataSource
, rendering it unusable.
After using the DeleteDataSource
operation, you can use the
GetDataSource
operation to verify that the status of the DataSource
changed to DELETED.
Specs:
- delete_data_source!(client :: ExAws.MachineLearning.t, input :: delete_data_source_input) :: ExAws.Request.JSON.success_t | no_return
Same as delete_data_source/2
but raise on error.
Specs:
- delete_evaluation(client :: ExAws.MachineLearning.t, input :: delete_evaluation_input) :: ExAws.Request.JSON.response_t
DeleteEvaluation
Assigns the DELETED
status to an Evaluation
, rendering it unusable.
After invoking the DeleteEvaluation
operation, you can use the
GetEvaluation
operation to verify that the status of the Evaluation
changed to DELETED
.
Specs:
- delete_evaluation!(client :: ExAws.MachineLearning.t, input :: delete_evaluation_input) :: ExAws.Request.JSON.success_t | no_return
Same as delete_evaluation/2
but raise on error.
Specs:
- delete_ml_model(client :: ExAws.MachineLearning.t, input :: delete_ml_model_input) :: ExAws.Request.JSON.response_t
DeleteMLModel
Assigns the DELETED status to an MLModel
, rendering it unusable.
After using the DeleteMLModel
operation, you can use the GetMLModel
operation to verify that the status of the MLModel
changed to DELETED.
Specs:
- delete_ml_model!(client :: ExAws.MachineLearning.t, input :: delete_ml_model_input) :: ExAws.Request.JSON.success_t | no_return
Same as delete_ml_model/2
but raise on error.
Specs:
- delete_realtime_endpoint(client :: ExAws.MachineLearning.t, input :: delete_realtime_endpoint_input) :: ExAws.Request.JSON.response_t
DeleteRealtimeEndpoint
Deletes a real time endpoint of an MLModel
.
Specs:
- delete_realtime_endpoint!(client :: ExAws.MachineLearning.t, input :: delete_realtime_endpoint_input) :: ExAws.Request.JSON.success_t | no_return
Same as delete_realtime_endpoint/2
but raise on error.
Specs:
- describe_batch_predictions(client :: ExAws.MachineLearning.t, input :: describe_batch_predictions_input) :: ExAws.Request.JSON.response_t
DescribeBatchPredictions
Returns a list of BatchPrediction
operations that match the search
criteria in the request.
Specs:
- describe_batch_predictions!(client :: ExAws.MachineLearning.t, input :: describe_batch_predictions_input) :: ExAws.Request.JSON.success_t | no_return
Same as describe_batch_predictions/2
but raise on error.
Specs:
- describe_data_sources(client :: ExAws.MachineLearning.t, input :: describe_data_sources_input) :: ExAws.Request.JSON.response_t
DescribeDataSources
Returns a list of DataSource
that match the search criteria in the
request.
Specs:
- describe_data_sources!(client :: ExAws.MachineLearning.t, input :: describe_data_sources_input) :: ExAws.Request.JSON.success_t | no_return
Same as describe_data_sources/2
but raise on error.
Specs:
- describe_evaluations(client :: ExAws.MachineLearning.t, input :: describe_evaluations_input) :: ExAws.Request.JSON.response_t
DescribeEvaluations
Returns a list of DescribeEvaluations
that match the search criteria in
the request.
Specs:
- describe_evaluations!(client :: ExAws.MachineLearning.t, input :: describe_evaluations_input) :: ExAws.Request.JSON.success_t | no_return
Same as describe_evaluations/2
but raise on error.
Specs:
- describe_ml_models(client :: ExAws.MachineLearning.t, input :: describe_ml_models_input) :: ExAws.Request.JSON.response_t
DescribeMLModels
Returns a list of MLModel
that match the search criteria in the request.
Specs:
- describe_ml_models!(client :: ExAws.MachineLearning.t, input :: describe_ml_models_input) :: ExAws.Request.JSON.success_t | no_return
Same as describe_ml_models/2
but raise on error.
Specs:
- get_batch_prediction(client :: ExAws.MachineLearning.t, input :: get_batch_prediction_input) :: ExAws.Request.JSON.response_t
GetBatchPrediction
Returns a BatchPrediction
that includes detailed metadata, status, and
data file information for a Batch Prediction
request.
Specs:
- get_batch_prediction!(client :: ExAws.MachineLearning.t, input :: get_batch_prediction_input) :: ExAws.Request.JSON.success_t | no_return
Same as get_batch_prediction/2
but raise on error.
Specs:
- get_data_source(client :: ExAws.MachineLearning.t, input :: get_data_source_input) :: ExAws.Request.JSON.response_t
GetDataSource
Returns a DataSource
that includes metadata and data file information, as
well as the current status of the DataSource
.
GetDataSource
provides results in normal or verbose format. The verbose
format adds the schema description and the list of files pointed to by the
DataSource to the normal format.
Specs:
- get_data_source!(client :: ExAws.MachineLearning.t, input :: get_data_source_input) :: ExAws.Request.JSON.success_t | no_return
Same as get_data_source/2
but raise on error.
Specs:
- get_evaluation(client :: ExAws.MachineLearning.t, input :: get_evaluation_input) :: ExAws.Request.JSON.response_t
GetEvaluation
Returns an Evaluation
that includes metadata as well as the current
status of the Evaluation
.
Specs:
- get_evaluation!(client :: ExAws.MachineLearning.t, input :: get_evaluation_input) :: ExAws.Request.JSON.success_t | no_return
Same as get_evaluation/2
but raise on error.
Specs:
- get_ml_model(client :: ExAws.MachineLearning.t, input :: get_ml_model_input) :: ExAws.Request.JSON.response_t
GetMLModel
Returns an MLModel
that includes detailed metadata, and data source
information as well as the current status of the MLModel
.
GetMLModel
provides results in normal or verbose format.
Specs:
- get_ml_model!(client :: ExAws.MachineLearning.t, input :: get_ml_model_input) :: ExAws.Request.JSON.success_t | no_return
Same as get_ml_model/2
but raise on error.
Specs:
- predict(client :: ExAws.MachineLearning.t, input :: predict_input) :: ExAws.Request.JSON.response_t
Predict
Generates a prediction for the observation using the specified MLModel
.
Note:
Specs:
- predict!(client :: ExAws.MachineLearning.t, input :: predict_input) :: ExAws.Request.JSON.success_t | no_return
Same as predict/2
but raise on error.
Specs:
- update_batch_prediction(client :: ExAws.MachineLearning.t, input :: update_batch_prediction_input) :: ExAws.Request.JSON.response_t
UpdateBatchPrediction
Updates the BatchPredictionName
of a BatchPrediction
.
You can use the GetBatchPrediction
operation to view the contents of the
updated data element.
Specs:
- update_batch_prediction!(client :: ExAws.MachineLearning.t, input :: update_batch_prediction_input) :: ExAws.Request.JSON.success_t | no_return
Same as update_batch_prediction/2
but raise on error.
Specs:
- update_data_source(client :: ExAws.MachineLearning.t, input :: update_data_source_input) :: ExAws.Request.JSON.response_t
UpdateDataSource
Updates the DataSourceName
of a DataSource
.
You can use the GetDataSource
operation to view the contents of the
updated data element.
Specs:
- update_data_source!(client :: ExAws.MachineLearning.t, input :: update_data_source_input) :: ExAws.Request.JSON.success_t | no_return
Same as update_data_source/2
but raise on error.
Specs:
- update_evaluation(client :: ExAws.MachineLearning.t, input :: update_evaluation_input) :: ExAws.Request.JSON.response_t
UpdateEvaluation
Updates the EvaluationName
of an Evaluation
.
You can use the GetEvaluation
operation to view the contents of the
updated data element.
Specs:
- update_evaluation!(client :: ExAws.MachineLearning.t, input :: update_evaluation_input) :: ExAws.Request.JSON.success_t | no_return
Same as update_evaluation/2
but raise on error.
Specs:
- update_ml_model(client :: ExAws.MachineLearning.t, input :: update_ml_model_input) :: ExAws.Request.JSON.response_t
UpdateMLModel
Updates the MLModelName
and the ScoreThreshold
of an MLModel
.
You can use the GetMLModel
operation to view the contents of the updated
data element.
Specs:
- update_ml_model!(client :: ExAws.MachineLearning.t, input :: update_ml_model_input) :: ExAws.Request.JSON.success_t | no_return
Same as update_ml_model/2
but raise on error.