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公开(公告)号：US20220171995A1
公开(公告)日：2022-06-02
申请号：US17106023
申请日：2020-11-27
Applicant: Amazon Technologies, Inc.
Inventor： Barath BALASUBRAMANIAN , Rahul BHOTIKA , Niels BROUWERS , Ranju DAS , Prakash KRISHNAN , Shaun Ryan James MCDOWELL , Anushri MAINTHIA , Rakesh Madhavan NAMBIAR , Anant PATEL , Avinash AGHORAM RAVICHANDRAN , Joaquin ZEPEDA SALVATIERRA , Gurumurthy SWAMINATHAN
Abstract: Techniques for anomaly detection are described. An exemplary method includes receiving one or more requests to train an anomaly detection machine learning model using feedback-based training, the request to indicate one or more of a type of analysis to perform, a model selection indication, and a configuration for a training dataset; training the anomaly detection machine learning model according to the one or more requests using the training data; performing feedback-based training on the trained anomaly detection machine learning model; and using the retrained anomaly detection machine learning model.
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公开(公告)号：US20220172100A1
公开(公告)日：2022-06-02
申请号：US17106026
申请日：2020-11-27
Applicant: Amazon Technologies, Inc.
Inventor： Barath BALASUBRAMANIAN , Rahul BHOTIKA , Niels BROUWERS , Ranju DAS , Prakash KRISHNAN , Shaun Ryan James MCDOWELL , Anushri MAINTHIA , Rakesh Madhavan NAMBIAR , Anant PATEL , Avinash AGHORAM RAVICHANDRAN , Joaquin ZEPEDA SALVATIERRA , Gurumurthy SWAMINATHAN
Abstract: Techniques for feedback-based training are described. An exemplary method includes receiving a request to perform feedback-based retraining, the request including one or more of an identifier of one or more models to retrain, an identifier of a dataset to use for retraining, an identifier of a dataset to use for testing, an indication of a threshold for an anomaly, an indication of how to display items to verify, and an indication of where to store historical information; applying the selected scoring machine learning model on an unlabeled dataset to generate, per dataset item of the unlabeled dataset, at least one of a score and a confidence for the score; providing a result of the application of the selected scoring machine learning model on an unlabeled dataset to request feedback in the form of a graphical user interface; receiving the requested feedback via the graphical user interface; adding data from the unlabeled dataset into the training dataset when the received requested feedback indicates a verified result; and retraining the selected scoring machine learning model using the training data with the added data from the unlabeled dataset.
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