Abstract: Technologies for generating a set of models for each account, where each model is a fine-grained, unsupervised behavior model trained for each user to monitor and detect anomalous patterns are described. An unsupervised training pipeline can generate user models, each being associated with one of multiple accounts and is trained to detect an anomalous pattern using feature data associated with the one account. Each account is associated with at least one of a user, a machine, or a service. An inference pipeline can detect a first anomalous pattern in first data associated with a first account using a first user model. The inference pipeline can detect a second anomalous pattern in second data associated with a second account using a second user model.

Abstract: Technologies for batching remote descriptors of serialized objects in streaming pipelines are described. One method of a first computing device generates a streaming batch of remote descriptors. Each remote descriptor uniquely identifies a contiguous block of a serialized object. The first computing device sends at least one of the remote descriptors to a second computing device before the streaming batch is completed. At least some contents of a contiguous block are obtained for storage at a second memory associated with the second computing device before the streaming batch is completed.

Abstract: Technologies for enabling downstream components to update upstream states in streaming pipelines are described. One method of a first computing device receives a remote promise object assigned to a first serialized object from a second computing device in the data center over a network fabric. The remote promise object uniquely identifies a first contiguous block of the first serialized object stored in a memory associated with the second computing device. The method obtains contents of the first contiguous block and sends contents of a second serialized object back to the second computing device to release the remote promise object.

Abstract: Technologies for enabling remote direct memory access (RDMA) transport of serialized objects in streaming pipelines are described. One method of a first computing device that stores a serialized object in a first memory can generate a remote descriptor associated with the serialized object. The remote descriptor uniquely identifies the location of the serialized object and a reference count token. The first computing device sends the remote descriptor to a second computing device in the data center over a network fabric. The second computing device uses the remote descriptor to obtain the contiguous block from the first memory for storage at a second memory associated with the second computing device. The value of the reference count token can be updated by receiving a message from the second computing device, and the remote descriptor can be released responsive to the value of the reference count token satisfying a threshold value.