Abstract: A device may include a processor configured to obtain a plurality of values for one or more cache population parameters associated with a distributed cache for a machine learning system. The processor may be further configured to provide the obtained plurality of values as inputs into a machine learning model trained to select a cache policy for the distributed cache; receive the selected cache policy from the machine learning model, wherein the selected cache policy includes a cache eviction policy that includes an eviction rule indicating when to remove particular data items from the distributed cache; and apply the received cache policy to the distributed cache.

Abstract: In some aspects, the techniques described herein relate to a method including: retrieving properties associated with a plurality of machine learning (ML) models; building a graph data structure using the properties, the graph data structure including nodes corresponding to the properties and edges representing connections between the properties; training a multi-stack message-passing graph neural network (MSMPGNN) using the graph data structure; inputting a graph fragment into the MSMPGNN; generating a prediction based on the graph fragment using the MSMPGNN; and displaying the prediction to a user.

Abstract: A device may include a processor configured to obtain a plurality of values for one or more cache population parameters associated with a distributed cache for a machine learning system. The processor may be further configured to provide the obtained plurality of values as inputs into a machine learning model trained to select a cache policy for the distributed cache; receive the selected cache policy from the machine learning model, wherein the selected cache policy includes a cache eviction policy that includes an eviction rule indicating when to remove particular data items from the distributed cache; and apply the received cache policy to the distributed cache.

Abstract: Techniques for determining anomalous user behavior in connection with an online application are disclosed. In one embodiment, a method is disclosed comprising obtaining user behavior data in connection with a user of an application, generating feature data using the obtained user behavior data, obtaining one or more user behavior anomaly predictions from one or more anomaly prediction models trained to output a user behavior anomaly prediction in response to the feature data. Each user behavior anomaly prediction indicates a probability that the user behavior is anomalous. A user behavior anomaly determination is made using the user behavior anomaly prediction(s).