Systems and methods for server load balancing based on correlated events
Methods and systems for balancing online stores amongst servers. Detecting a flash sale associated with a first online store. Identifying an occurrence of a first event correlated to the flash sale associated with the first online store. Identifying a second online store associated with a second event corresponding to the first event. Responsive to identifying the second online store associated with the second event corresponding to the first event, moving the second online store from a first server to a second server.
United States Patent Application: 20230059656
Status: Active
Systems & Methods for Detecting Non-causal Dependencies in Machine Learning Models
A non-causal dependency in a machine learning model can bias the performance of the machine learning model. Systems and methods for detecting non-causal dependencies in machine learning models are provided. According to an embodiment, a method includes generating a plurality of data samples from a particular data sample, the plurality of data samples including a modified data sample that differs from the particular data sample by non-causal data, the non-causal data having a non-causal relationship to the output of a machine learning model. The method also includes generating a plurality of results by inputting the plurality of data samples into the machine learning model. The method further includes determining, based on a comparison of the plurality of results, if the machine learning model is dependent on the non-causal data.
United States Patent Application: 20210182730
Status: Abandoned