我想办法修好了。
在SageMaker documentation中，有两个类可用于加载已部署的线性学习器

选项1：使用Sagemaker. LinearLearnerModel从S3人工神经网络建立预测器

根据文档，此类：
"参考线性学习器s3模型数据"
您可以按如下方式使用它：

步骤1：使用S3上托管的artificats构建线性学习器模型

from sagemaker import LinearLearnerModel, get_execution_role, Session
import boto3
sess = boto3.Session(region_name=region_name)
sagemaker_session = Session(boto_session=sess)
role = get_execution_role(sagemaker_session)
model = LinearLearnerModel(model_data, role, sagemaker_session=sagemaker_session)

步骤2：将模型部署到端点（在本例中，它是一个无服务器端点）

my_serverless_inference_config = ServerlessInferenceConfig(memory_size_in_mb=2048, max_concurrency=1)
linear_predictor = model .deploy(endpoint_name=my_endpoint_name,serverless_inference_config=my_serverless_inference_config,serializer=CSVSerializer(), deserializer=JSONDeserializer())
from sagemaker.predictor import csv_serializer, json_deserializer
linear_regressor.serializer = csv_serializer
linear_regressor.deserializer = json_deserializer

步骤3：使用部署函数返回的sagemaker. predictor对象

result = linear_predictor.predict(X_test)
#Iterate the result JSON to get an NP array of all the predictions so we can compare to Y test
predictions = np.array([res['score'] for res in result['predictions']])

选项2：使用LinearLearnerPredictor根据已有的端点构建预测值

如果模型具有已部署的端点，则可以使用此选项

from sagemaker import LinearLearnerPredictor, get_execution_role, Session
import boto3
sess = boto3.Session(region_name=region_name)
sagemaker_session = Session(boto_session=sess)
role = get_execution_role(sagemaker_session)
predictor = LinearLearnerPredictor(endpoint_name, sagemaker_session=sagemaker_session)