MachineLearningClient

Index > MachineLearning > MachineLearningClient

Auto-generated documentation for MachineLearning type annotations stubs module mypy-boto3-machinelearning.

MachineLearningClient

Type annotations and code completion for boto3.client("machinelearning"). boto3 documentation

# MachineLearningClient usage example

from boto3.session import Session
from mypy_boto3_machinelearning.client import MachineLearningClient

def get_machinelearning_client() -> MachineLearningClient:
    return Session().client("machinelearning")

Exceptions

boto3 client exceptions are generated in runtime. This class provides code completion for boto3.client("machinelearning").exceptions structure.

# Exceptions.exceptions usage example

client = boto3.client("machinelearning")

try:
    do_something(client)
except (
    client.exceptions.ClientError,
    client.exceptions.IdempotentParameterMismatchException,
    client.exceptions.InternalServerException,
    client.exceptions.InvalidInputException,
    client.exceptions.InvalidTagException,
    client.exceptions.LimitExceededException,
    client.exceptions.PredictorNotMountedException,
    client.exceptions.ResourceNotFoundException,
    client.exceptions.TagLimitExceededException,
) as e:
    print(e)

# Exceptions.exceptions type checking example

from mypy_boto3_machinelearning.client import Exceptions

def handle_error(exc: Exceptions.ClientError) -> None:
    ...

Methods

add_tags

Adds one or more tags to an object, up to a limit of 10.

Type annotations and code completion for boto3.client("machinelearning").add_tags method. boto3 documentation

# add_tags method definition

def add_tags(
    self,
    *,
    Tags: Sequence[TagTypeDef],  # (1)
    ResourceId: str,
    ResourceType: TaggableResourceTypeType,  # (2)
) -> AddTagsOutputTypeDef:  # (3)
    ...

1. See TagTypeDef
2. See TaggableResourceTypeType
3. See AddTagsOutputTypeDef

# add_tags method usage example with argument unpacking

kwargs: AddTagsInputRequestTypeDef = {  # (1)
    "Tags": ...,
    "ResourceId": ...,
    "ResourceType": ...,
}

parent.add_tags(**kwargs)

1. See AddTagsInputRequestTypeDef

can_paginate

Check if an operation can be paginated.

Type annotations and code completion for boto3.client("machinelearning").can_paginate method. boto3 documentation

# can_paginate method definition

def can_paginate(
    self,
    operation_name: str,
) -> bool:
    ...

close

Closes underlying endpoint connections.

Type annotations and code completion for boto3.client("machinelearning").close method. boto3 documentation

# close method definition

def close(
    self,
) -> None:
    ...

create_batch_prediction

Generates predictions for a group of observations.

Type annotations and code completion for boto3.client("machinelearning").create_batch_prediction method. boto3 documentation

# create_batch_prediction method definition

def create_batch_prediction(
    self,
    *,
    BatchPredictionId: str,
    MLModelId: str,
    BatchPredictionDataSourceId: str,
    OutputUri: str,
    BatchPredictionName: str = ...,
) -> CreateBatchPredictionOutputTypeDef:  # (1)
    ...

1. See CreateBatchPredictionOutputTypeDef

# create_batch_prediction method usage example with argument unpacking

kwargs: CreateBatchPredictionInputRequestTypeDef = {  # (1)
    "BatchPredictionId": ...,
    "MLModelId": ...,
    "BatchPredictionDataSourceId": ...,
    "OutputUri": ...,
}

parent.create_batch_prediction(**kwargs)

1. See CreateBatchPredictionInputRequestTypeDef

create_data_source_from_rds

Creates a DataSource object from an Amazon Relational Database Service <http://aws.amazon.com/rds/>__ (Amazon RDS).

Type annotations and code completion for boto3.client("machinelearning").create_data_source_from_rds method. boto3 documentation

# create_data_source_from_rds method definition

def create_data_source_from_rds(
    self,
    *,
    DataSourceId: str,
    RDSData: RDSDataSpecTypeDef,  # (1)
    RoleARN: str,
    DataSourceName: str = ...,
    ComputeStatistics: bool = ...,
) -> CreateDataSourceFromRDSOutputTypeDef:  # (2)
    ...

1. See RDSDataSpecTypeDef
2. See CreateDataSourceFromRDSOutputTypeDef

# create_data_source_from_rds method usage example with argument unpacking

kwargs: CreateDataSourceFromRDSInputRequestTypeDef = {  # (1)
    "DataSourceId": ...,
    "RDSData": ...,
    "RoleARN": ...,
}

parent.create_data_source_from_rds(**kwargs)

1. See CreateDataSourceFromRDSInputRequestTypeDef

create_data_source_from_redshift

Creates a DataSource from a database hosted on an Amazon Redshift cluster.

Type annotations and code completion for boto3.client("machinelearning").create_data_source_from_redshift method. boto3 documentation

# create_data_source_from_redshift method definition

def create_data_source_from_redshift(
    self,
    *,
    DataSourceId: str,
    DataSpec: RedshiftDataSpecTypeDef,  # (1)
    RoleARN: str,
    DataSourceName: str = ...,
    ComputeStatistics: bool = ...,
) -> CreateDataSourceFromRedshiftOutputTypeDef:  # (2)
    ...

1. See RedshiftDataSpecTypeDef
2. See CreateDataSourceFromRedshiftOutputTypeDef

# create_data_source_from_redshift method usage example with argument unpacking

kwargs: CreateDataSourceFromRedshiftInputRequestTypeDef = {  # (1)
    "DataSourceId": ...,
    "DataSpec": ...,
    "RoleARN": ...,
}

parent.create_data_source_from_redshift(**kwargs)

1. See CreateDataSourceFromRedshiftInputRequestTypeDef

create_data_source_from_s3

Creates a DataSource object.

Type annotations and code completion for boto3.client("machinelearning").create_data_source_from_s3 method. boto3 documentation

# create_data_source_from_s3 method definition

def create_data_source_from_s3(
    self,
    *,
    DataSourceId: str,
    DataSpec: S3DataSpecTypeDef,  # (1)
    DataSourceName: str = ...,
    ComputeStatistics: bool = ...,
) -> CreateDataSourceFromS3OutputTypeDef:  # (2)
    ...

1. See S3DataSpecTypeDef
2. See CreateDataSourceFromS3OutputTypeDef

# create_data_source_from_s3 method usage example with argument unpacking

kwargs: CreateDataSourceFromS3InputRequestTypeDef = {  # (1)
    "DataSourceId": ...,
    "DataSpec": ...,
}

parent.create_data_source_from_s3(**kwargs)

1. See CreateDataSourceFromS3InputRequestTypeDef

create_evaluation

Creates a new Evaluation of an MLModel.

Type annotations and code completion for boto3.client("machinelearning").create_evaluation method. boto3 documentation

# create_evaluation method definition

def create_evaluation(
    self,
    *,
    EvaluationId: str,
    MLModelId: str,
    EvaluationDataSourceId: str,
    EvaluationName: str = ...,
) -> CreateEvaluationOutputTypeDef:  # (1)
    ...

1. See CreateEvaluationOutputTypeDef

# create_evaluation method usage example with argument unpacking

kwargs: CreateEvaluationInputRequestTypeDef = {  # (1)
    "EvaluationId": ...,
    "MLModelId": ...,
    "EvaluationDataSourceId": ...,
}

parent.create_evaluation(**kwargs)

1. See CreateEvaluationInputRequestTypeDef

create_ml_model

Creates a new MLModel using the DataSource and the recipe as information sources.

Type annotations and code completion for boto3.client("machinelearning").create_ml_model method. boto3 documentation

# create_ml_model method definition

def create_ml_model(
    self,
    *,
    MLModelId: str,
    MLModelType: MLModelTypeType,  # (1)
    TrainingDataSourceId: str,
    MLModelName: str = ...,
    Parameters: Mapping[str, str] = ...,
    Recipe: str = ...,
    RecipeUri: str = ...,
) -> CreateMLModelOutputTypeDef:  # (2)
    ...

1. See MLModelTypeType
2. See CreateMLModelOutputTypeDef

# create_ml_model method usage example with argument unpacking

kwargs: CreateMLModelInputRequestTypeDef = {  # (1)
    "MLModelId": ...,
    "MLModelType": ...,
    "TrainingDataSourceId": ...,
}

parent.create_ml_model(**kwargs)

1. See CreateMLModelInputRequestTypeDef

create_realtime_endpoint

Creates a real-time endpoint for the MLModel.

Type annotations and code completion for boto3.client("machinelearning").create_realtime_endpoint method. boto3 documentation

# create_realtime_endpoint method definition

def create_realtime_endpoint(
    self,
    *,
    MLModelId: str,
) -> CreateRealtimeEndpointOutputTypeDef:  # (1)
    ...

1. See CreateRealtimeEndpointOutputTypeDef

# create_realtime_endpoint method usage example with argument unpacking

kwargs: CreateRealtimeEndpointInputRequestTypeDef = {  # (1)
    "MLModelId": ...,
}

parent.create_realtime_endpoint(**kwargs)

1. See CreateRealtimeEndpointInputRequestTypeDef

delete_batch_prediction

Assigns the DELETED status to a BatchPrediction, rendering it unusable.

Type annotations and code completion for boto3.client("machinelearning").delete_batch_prediction method. boto3 documentation

# delete_batch_prediction method definition

def delete_batch_prediction(
    self,
    *,
    BatchPredictionId: str,
) -> DeleteBatchPredictionOutputTypeDef:  # (1)
    ...

1. See DeleteBatchPredictionOutputTypeDef

# delete_batch_prediction method usage example with argument unpacking

kwargs: DeleteBatchPredictionInputRequestTypeDef = {  # (1)
    "BatchPredictionId": ...,
}

parent.delete_batch_prediction(**kwargs)

1. See DeleteBatchPredictionInputRequestTypeDef

delete_data_source

Assigns the DELETED status to a DataSource, rendering it unusable.

Type annotations and code completion for boto3.client("machinelearning").delete_data_source method. boto3 documentation

# delete_data_source method definition

def delete_data_source(
    self,
    *,
    DataSourceId: str,
) -> DeleteDataSourceOutputTypeDef:  # (1)
    ...

1. See DeleteDataSourceOutputTypeDef

# delete_data_source method usage example with argument unpacking

kwargs: DeleteDataSourceInputRequestTypeDef = {  # (1)
    "DataSourceId": ...,
}

parent.delete_data_source(**kwargs)

1. See DeleteDataSourceInputRequestTypeDef

delete_evaluation

Assigns the DELETED status to an Evaluation, rendering it unusable.

Type annotations and code completion for boto3.client("machinelearning").delete_evaluation method. boto3 documentation

# delete_evaluation method definition

def delete_evaluation(
    self,
    *,
    EvaluationId: str,
) -> DeleteEvaluationOutputTypeDef:  # (1)
    ...

1. See DeleteEvaluationOutputTypeDef

# delete_evaluation method usage example with argument unpacking

kwargs: DeleteEvaluationInputRequestTypeDef = {  # (1)
    "EvaluationId": ...,
}

parent.delete_evaluation(**kwargs)

1. See DeleteEvaluationInputRequestTypeDef

delete_ml_model

Assigns the DELETED status to an MLModel, rendering it unusable.

Type annotations and code completion for boto3.client("machinelearning").delete_ml_model method. boto3 documentation

# delete_ml_model method definition

def delete_ml_model(
    self,
    *,
    MLModelId: str,
) -> DeleteMLModelOutputTypeDef:  # (1)
    ...

1. See DeleteMLModelOutputTypeDef

# delete_ml_model method usage example with argument unpacking

kwargs: DeleteMLModelInputRequestTypeDef = {  # (1)
    "MLModelId": ...,
}

parent.delete_ml_model(**kwargs)

1. See DeleteMLModelInputRequestTypeDef

delete_realtime_endpoint

Deletes a real time endpoint of an MLModel.

Type annotations and code completion for boto3.client("machinelearning").delete_realtime_endpoint method. boto3 documentation

# delete_realtime_endpoint method definition

def delete_realtime_endpoint(
    self,
    *,
    MLModelId: str,
) -> DeleteRealtimeEndpointOutputTypeDef:  # (1)
    ...

1. See DeleteRealtimeEndpointOutputTypeDef

# delete_realtime_endpoint method usage example with argument unpacking

kwargs: DeleteRealtimeEndpointInputRequestTypeDef = {  # (1)
    "MLModelId": ...,
}

parent.delete_realtime_endpoint(**kwargs)

1. See DeleteRealtimeEndpointInputRequestTypeDef

delete_tags

Deletes the specified tags associated with an ML object.

Type annotations and code completion for boto3.client("machinelearning").delete_tags method. boto3 documentation

# delete_tags method definition

def delete_tags(
    self,
    *,
    TagKeys: Sequence[str],
    ResourceId: str,
    ResourceType: TaggableResourceTypeType,  # (1)
) -> DeleteTagsOutputTypeDef:  # (2)
    ...

1. See TaggableResourceTypeType
2. See DeleteTagsOutputTypeDef

# delete_tags method usage example with argument unpacking

kwargs: DeleteTagsInputRequestTypeDef = {  # (1)
    "TagKeys": ...,
    "ResourceId": ...,
    "ResourceType": ...,
}

parent.delete_tags(**kwargs)

1. See DeleteTagsInputRequestTypeDef

describe_batch_predictions

Returns a list of BatchPrediction operations that match the search criteria in the request.

Type annotations and code completion for boto3.client("machinelearning").describe_batch_predictions method. boto3 documentation

# describe_batch_predictions method definition

def describe_batch_predictions(
    self,
    *,
    FilterVariable: BatchPredictionFilterVariableType = ...,  # (1)
    EQ: str = ...,
    GT: str = ...,
    LT: str = ...,
    GE: str = ...,
    LE: str = ...,
    NE: str = ...,
    Prefix: str = ...,
    SortOrder: SortOrderType = ...,  # (2)
    NextToken: str = ...,
    Limit: int = ...,
) -> DescribeBatchPredictionsOutputTypeDef:  # (3)
    ...

1. See BatchPredictionFilterVariableType
2. See SortOrderType
3. See DescribeBatchPredictionsOutputTypeDef

# describe_batch_predictions method usage example with argument unpacking

kwargs: DescribeBatchPredictionsInputRequestTypeDef = {  # (1)
    "FilterVariable": ...,
}

parent.describe_batch_predictions(**kwargs)

1. See DescribeBatchPredictionsInputRequestTypeDef

describe_data_sources

Returns a list of DataSource that match the search criteria in the request.

Type annotations and code completion for boto3.client("machinelearning").describe_data_sources method. boto3 documentation

# describe_data_sources method definition

def describe_data_sources(
    self,
    *,
    FilterVariable: DataSourceFilterVariableType = ...,  # (1)
    EQ: str = ...,
    GT: str = ...,
    LT: str = ...,
    GE: str = ...,
    LE: str = ...,
    NE: str = ...,
    Prefix: str = ...,
    SortOrder: SortOrderType = ...,  # (2)
    NextToken: str = ...,
    Limit: int = ...,
) -> DescribeDataSourcesOutputTypeDef:  # (3)
    ...

1. See DataSourceFilterVariableType
2. See SortOrderType
3. See DescribeDataSourcesOutputTypeDef

# describe_data_sources method usage example with argument unpacking

kwargs: DescribeDataSourcesInputRequestTypeDef = {  # (1)
    "FilterVariable": ...,
}

parent.describe_data_sources(**kwargs)

1. See DescribeDataSourcesInputRequestTypeDef

describe_evaluations

Returns a list of DescribeEvaluations that match the search criteria in the request.

Type annotations and code completion for boto3.client("machinelearning").describe_evaluations method. boto3 documentation

# describe_evaluations method definition

def describe_evaluations(
    self,
    *,
    FilterVariable: EvaluationFilterVariableType = ...,  # (1)
    EQ: str = ...,
    GT: str = ...,
    LT: str = ...,
    GE: str = ...,
    LE: str = ...,
    NE: str = ...,
    Prefix: str = ...,
    SortOrder: SortOrderType = ...,  # (2)
    NextToken: str = ...,
    Limit: int = ...,
) -> DescribeEvaluationsOutputTypeDef:  # (3)
    ...

1. See EvaluationFilterVariableType
2. See SortOrderType
3. See DescribeEvaluationsOutputTypeDef

# describe_evaluations method usage example with argument unpacking

kwargs: DescribeEvaluationsInputRequestTypeDef = {  # (1)
    "FilterVariable": ...,
}

parent.describe_evaluations(**kwargs)

1. See DescribeEvaluationsInputRequestTypeDef

describe_ml_models

Returns a list of MLModel that match the search criteria in the request.

Type annotations and code completion for boto3.client("machinelearning").describe_ml_models method. boto3 documentation

# describe_ml_models method definition

def describe_ml_models(
    self,
    *,
    FilterVariable: MLModelFilterVariableType = ...,  # (1)
    EQ: str = ...,
    GT: str = ...,
    LT: str = ...,
    GE: str = ...,
    LE: str = ...,
    NE: str = ...,
    Prefix: str = ...,
    SortOrder: SortOrderType = ...,  # (2)
    NextToken: str = ...,
    Limit: int = ...,
) -> DescribeMLModelsOutputTypeDef:  # (3)
    ...

1. See MLModelFilterVariableType
2. See SortOrderType
3. See DescribeMLModelsOutputTypeDef

# describe_ml_models method usage example with argument unpacking

kwargs: DescribeMLModelsInputRequestTypeDef = {  # (1)
    "FilterVariable": ...,
}

parent.describe_ml_models(**kwargs)

1. See DescribeMLModelsInputRequestTypeDef

describe_tags

Describes one or more of the tags for your Amazon ML object.

Type annotations and code completion for boto3.client("machinelearning").describe_tags method. boto3 documentation

# describe_tags method definition

def describe_tags(
    self,
    *,
    ResourceId: str,
    ResourceType: TaggableResourceTypeType,  # (1)
) -> DescribeTagsOutputTypeDef:  # (2)
    ...

1. See TaggableResourceTypeType
2. See DescribeTagsOutputTypeDef

# describe_tags method usage example with argument unpacking

kwargs: DescribeTagsInputRequestTypeDef = {  # (1)
    "ResourceId": ...,
    "ResourceType": ...,
}

parent.describe_tags(**kwargs)

1. See DescribeTagsInputRequestTypeDef

generate_presigned_url

Generate a presigned url given a client, its method, and arguments.

Type annotations and code completion for boto3.client("machinelearning").generate_presigned_url method. boto3 documentation

# generate_presigned_url method definition

def generate_presigned_url(
    self,
    ClientMethod: str,
    Params: Mapping[str, Any] = ...,
    ExpiresIn: int = 3600,
    HttpMethod: str = ...,
) -> str:
    ...

get_batch_prediction

Returns a BatchPrediction that includes detailed metadata, status, and data file information for a Batch Prediction request.

Type annotations and code completion for boto3.client("machinelearning").get_batch_prediction method. boto3 documentation

# get_batch_prediction method definition

def get_batch_prediction(
    self,
    *,
    BatchPredictionId: str,
) -> GetBatchPredictionOutputTypeDef:  # (1)
    ...

1. See GetBatchPredictionOutputTypeDef

# get_batch_prediction method usage example with argument unpacking

kwargs: GetBatchPredictionInputRequestTypeDef = {  # (1)
    "BatchPredictionId": ...,
}

parent.get_batch_prediction(**kwargs)

1. See GetBatchPredictionInputRequestTypeDef

get_data_source

Returns a DataSource that includes metadata and data file information, as well as the current status of the DataSource.

Type annotations and code completion for boto3.client("machinelearning").get_data_source method. boto3 documentation

# get_data_source method definition

def get_data_source(
    self,
    *,
    DataSourceId: str,
    Verbose: bool = ...,
) -> GetDataSourceOutputTypeDef:  # (1)
    ...

1. See GetDataSourceOutputTypeDef

# get_data_source method usage example with argument unpacking

kwargs: GetDataSourceInputRequestTypeDef = {  # (1)
    "DataSourceId": ...,
}

parent.get_data_source(**kwargs)

1. See GetDataSourceInputRequestTypeDef

get_evaluation

Returns an Evaluation that includes metadata as well as the current status of the Evaluation.

Type annotations and code completion for boto3.client("machinelearning").get_evaluation method. boto3 documentation

# get_evaluation method definition

def get_evaluation(
    self,
    *,
    EvaluationId: str,
) -> GetEvaluationOutputTypeDef:  # (1)
    ...

1. See GetEvaluationOutputTypeDef

# get_evaluation method usage example with argument unpacking

kwargs: GetEvaluationInputRequestTypeDef = {  # (1)
    "EvaluationId": ...,
}

parent.get_evaluation(**kwargs)

1. See GetEvaluationInputRequestTypeDef

get_ml_model

Returns an MLModel that includes detailed metadata, data source information, and the current status of the MLModel.

Type annotations and code completion for boto3.client("machinelearning").get_ml_model method. boto3 documentation

# get_ml_model method definition

def get_ml_model(
    self,
    *,
    MLModelId: str,
    Verbose: bool = ...,
) -> GetMLModelOutputTypeDef:  # (1)
    ...

1. See GetMLModelOutputTypeDef

# get_ml_model method usage example with argument unpacking

kwargs: GetMLModelInputRequestTypeDef = {  # (1)
    "MLModelId": ...,
}

parent.get_ml_model(**kwargs)

1. See GetMLModelInputRequestTypeDef

predict

Generates a prediction for the observation using the specified ML Model.

Type annotations and code completion for boto3.client("machinelearning").predict method. boto3 documentation

# predict method definition

def predict(
    self,
    *,
    MLModelId: str,
    Record: Mapping[str, str],
    PredictEndpoint: str,
) -> PredictOutputTypeDef:  # (1)
    ...

1. See PredictOutputTypeDef

# predict method usage example with argument unpacking

kwargs: PredictInputRequestTypeDef = {  # (1)
    "MLModelId": ...,
    "Record": ...,
    "PredictEndpoint": ...,
}

parent.predict(**kwargs)

1. See PredictInputRequestTypeDef

update_batch_prediction

Updates the BatchPredictionName of a BatchPrediction.

Type annotations and code completion for boto3.client("machinelearning").update_batch_prediction method. boto3 documentation

# update_batch_prediction method definition

def update_batch_prediction(
    self,
    *,
    BatchPredictionId: str,
    BatchPredictionName: str,
) -> UpdateBatchPredictionOutputTypeDef:  # (1)
    ...

1. See UpdateBatchPredictionOutputTypeDef

# update_batch_prediction method usage example with argument unpacking

kwargs: UpdateBatchPredictionInputRequestTypeDef = {  # (1)
    "BatchPredictionId": ...,
    "BatchPredictionName": ...,
}

parent.update_batch_prediction(**kwargs)

1. See UpdateBatchPredictionInputRequestTypeDef

update_data_source

Updates the DataSourceName of a DataSource.

Type annotations and code completion for boto3.client("machinelearning").update_data_source method. boto3 documentation

# update_data_source method definition

def update_data_source(
    self,
    *,
    DataSourceId: str,
    DataSourceName: str,
) -> UpdateDataSourceOutputTypeDef:  # (1)
    ...

1. See UpdateDataSourceOutputTypeDef

# update_data_source method usage example with argument unpacking

kwargs: UpdateDataSourceInputRequestTypeDef = {  # (1)
    "DataSourceId": ...,
    "DataSourceName": ...,
}

parent.update_data_source(**kwargs)

1. See UpdateDataSourceInputRequestTypeDef

update_evaluation

Updates the EvaluationName of an Evaluation.

Type annotations and code completion for boto3.client("machinelearning").update_evaluation method. boto3 documentation

# update_evaluation method definition

def update_evaluation(
    self,
    *,
    EvaluationId: str,
    EvaluationName: str,
) -> UpdateEvaluationOutputTypeDef:  # (1)
    ...

1. See UpdateEvaluationOutputTypeDef

# update_evaluation method usage example with argument unpacking

kwargs: UpdateEvaluationInputRequestTypeDef = {  # (1)
    "EvaluationId": ...,
    "EvaluationName": ...,
}

parent.update_evaluation(**kwargs)

1. See UpdateEvaluationInputRequestTypeDef

update_ml_model

Updates the MLModelName and the ScoreThreshold of an MLModel.

Type annotations and code completion for boto3.client("machinelearning").update_ml_model method. boto3 documentation

# update_ml_model method definition

def update_ml_model(
    self,
    *,
    MLModelId: str,
    MLModelName: str = ...,
    ScoreThreshold: float = ...,
) -> UpdateMLModelOutputTypeDef:  # (1)
    ...

1. See UpdateMLModelOutputTypeDef

# update_ml_model method usage example with argument unpacking

kwargs: UpdateMLModelInputRequestTypeDef = {  # (1)
    "MLModelId": ...,
}

parent.update_ml_model(**kwargs)

1. See UpdateMLModelInputRequestTypeDef

get_paginator

Type annotations and code completion for boto3.client("machinelearning").get_paginator method with overloads.

• client.get_paginator("describe_batch_predictions") -> DescribeBatchPredictionsPaginator
• client.get_paginator("describe_data_sources") -> DescribeDataSourcesPaginator
• client.get_paginator("describe_evaluations") -> DescribeEvaluationsPaginator
• client.get_paginator("describe_ml_models") -> DescribeMLModelsPaginator

get_waiter

Type annotations and code completion for boto3.client("machinelearning").get_waiter method with overloads.

• client.get_waiter("batch_prediction_available") -> BatchPredictionAvailableWaiter
• client.get_waiter("data_source_available") -> DataSourceAvailableWaiter
• client.get_waiter("evaluation_available") -> EvaluationAvailableWaiter
• client.get_waiter("ml_model_available") -> MLModelAvailableWaiter