问题描述
我想部署一个实时预测机器学习模型，用于使用 sagemaker 进行欺诈检测。
我使用 sagemaker jupyter 实例来：
-load my training data from s3 contains transactions
-preprocessing data and features engineering (i use category_encoders to encode the categorical value)
-training the model and configure the endpoint
对于推理步骤，我使用了一个 lambda 函数，它调用我的端点来获取每个实时交易的预测。
should i calculte again all the features for this real time transactions in lambda function ?
for the features when i use category_encoders with fit_transform() function to transform my categorical feature to numerical one,what should I do because the result will not be the same as training set?
is there another method not to redo the calculation of the features in the inference step?
解决方法
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