Abstract: Using user feedback for attack path analysis in an anomaly detection framework, including: performing an attack path analysis for a cloud deployment; receiving, from a user, user feedback for an attack vector of the attack path analysis; and initiating, based on the user feedback, a workflow for modifying one or more parameters for generating the attack path analysis.

Abstract: Leveraging generative artificial intelligence (‘AI’) for securing a monitored deployment, including: receiving natural language input associated with the monitored deployment, the monitored deployment monitored by a monitoring tool; and receiving, from a generative AI application, a response to the natural language input, wherein: the generative AI application accesses publicly available information as well as data sources associated with the monitoring tool; and the response is generated based at least in part on information contained in the data sources associated with the monitoring tool.

Abstract: Integrating a natural language interface into an anomaly detection framework, including: detecting, by an anomaly detection framework, an occurrence of an event associated with one or more assets that are being monitored by an anomaly detection framework; generating, based on information associated with the detected event, one or more natural language inputs; and submitting, to a natural language interface, the one or more natural language inputs.

Abstract: A natural language interface for an anomaly detection framework, including: receiving a natural language input associated with a cloud deployment; generating a query corresponding to the natural language input by disambiguating at least a portion of the natural language input based on data describing activity associated with an anomaly detection framework monitoring the cloud deployment; and providing, based on a response to the query, a response to the natural language input.

Abstract: The disclosed apparatus may include unmanned aerial vehicle having (1) a flight system that causes movement and hovering of the unmanned aerial vehicle, (2) a coupling mechanism that interacts with a corresponding coupling mechanism of an electronic device for carrying the electronic device from a remote location to a mount of an infrastructure component, and (3) an extension mechanism connecting the coupling mechanism to the flight system, wherein the extension mechanism dynamically extends the coupling mechanism from the flight system to facilitate installation of the electronic device to the mount while the unmanned aerial vehicle hovers. Various other apparatuses, devices, and methods are also disclosed.

Abstract: Providing a data lake-enabled security platform, including: storing security data associated with a customer in a data lake comprising a plurality of storage environments implemented in different cloud environments of a plurality of cloud environments; generating, based on a plurality of records in the security data, an abstracted security record describing one or more derived insights of the security data; and providing access to the abstracted security record to one or more users associated with the customer.

Abstract: A guided anomaly detection framework, including: gathering data describing activity associated with an anomaly detection framework monitoring a cloud deployment; generating, based on the data, a prompt describing one or more natural language inputs for a security workflow, wherein each of the one or more natural language inputs corresponds to a query for information related to the cloud deployment; and providing a selected natural language input to a natural language interface.

Abstract: A machine learning model is trained. A feature importance metric is determined for each machine learning feature of a plurality of machine learning features of the machine learning model. Based on the feature importance metrics, one or more machine learning features of the plurality of machine learning features of the machine learning model are managed.

Abstract: Systems and methods for classifying images (e.g., ads) are described. An image is accessed. Optical character recognition is performed on at least a first portion of the image. Image recognition is performed via a convolutional neural network on at least a second portion of the image. At least one class for the image is automatically identified, via a fully connected neural network, based on one or more predictions, each of the one or more predictions being based on both the optical character recognition and the image recognition. Finally, the at least one class identified for the image is output.

Abstract: A machine learning model is trained. A feature importance metric is determined for each machine learning feature of a plurality of machine learning features of the machine learning model. Based on the feature importance metrics, one or more machine learning features of the plurality of machine learning features of the machine learning model are managed.

Abstract: The disclosed apparatus may include (1) a mount of an infrastructure component, where the mount includes an installation surface that contacts an installed electronic device, and (2) a coupling mechanism that (a) provides a force causing a corresponding installation surface of the installed electronic device to maintain contact with the installation device, and (b) delivers electrical power via the installation surface to the installed electronic device. Various other apparatuses, devices, and methods are also disclosed.

Abstract: A system and method for multi-channel communication with dual in-line memory modules (“DIMMs”) is disclosed. The system retrieves information characterizing a plurality of memory channels, each of each is configurable to facilitate data communication between a DIMM and a memory controller with associated memory channel interfaces. Based on the retrieved information, one of the memory channels is designated as the active memory channel, granting the designated memory channel the ability to issue memory requests or transactions to the DIMM. On a periodic or as-needed basis (e.g., when the active memory channel is stalled or nearly stalled), the system determines whether to designate a different of the memory channels as the active memory channel, thereby enabling the newly-designated active memory channel the ability to issue memory requests or transactions to the DIMM. In some embodiments, only one of the memory channels is active at a time for communication with each DIMM.

Abstract: In one embodiment, a method includes establishing access to first and second different computing systems. A machine learning model is assigned for training to the first computing system, and the first computing system creates a check-point during training in response to a first predefined triggering event. The check-point may be a record of an execution state in the training of the machine learning model by the first computing system. In response to a second predefined triggering event, the training of the machine learning model on the first computing system is halted, and in response to a third predefined triggering event, the training of the machine learning model is transferred to the second computing system, which continues training the machine learning model starting from the execution state recorded by the check-point.

Abstract: It is determined that a selected server among a pool of servers is eligible to be utilized for machine learning training. At least the selected server is utilized to train at least a portion of a machine learning model. It is determined that the selected server among the pool of servers is no longer eligible to be utilized for machine learning training. A training state of the machine learning model is saved. The selected server is returned for other use in the pool of servers.

Abstract: A machine learning model is trained. A feature importance metric is determined for each machine learning feature of a plurality of machine learning features of the machine learning model. Based on the feature importance metrics, one or more machine learning features of the plurality of machine learning features of the machine learning model are managed.

Abstract: Systems and methods for classifying images (e.g., ads) are described. An image is accessed. Optical character recognition is performed on at least a first portion of the image. Image recognition is performed via a convolutional neural network on at least a second portion of the image. At least one class for the image is automatically identified, via a fully connected neural network, based on one or more predictions, each of the one or more predictions being based on both the optical character recognition and the image recognition. Finally, the at least one class identified for the image is output.

Abstract: A system and method for multi-channel communication with dual in-line memory modules (“DIMMs”) is disclosed. The system retrieves information characterizing a plurality of memory channels, each of each is configurable to facilitate data communication between a DIMM and a memory controller with associated memory channel interfaces. Based on the retrieved information, one of the memory channels is designated as the active memory channel, granting the designated memory channel the ability to issue memory requests or transactions to the DIMM. On a periodic or as-needed basis (e.g., when the active memory channel is stalled or nearly stalled), the system determines whether to designate a different of the memory channels as the active memory channel, thereby enabling the newly-designated active memory channel the ability to issue memory requests or transactions to the DIMM. In some embodiments, only one of the memory channels is active at a time for communication with each DIMM.

Abstract: In one embodiment, a method includes establishing access to first and second different computing systems. A machine learning model is assigned for training to the first computing system, and the first computing system creates a check-point during training in response to a first predefined triggering event. The check-point may be a record of an execution state in the training of the machine learning model by the first computing system. In response to a second predefined triggering event, the training of the machine learning model on the first computing system is halted, and in response to a third predefined triggering event, the training of the machine learning model is transferred to the second computing system, which continues training the machine learning model starting from the execution state recorded by the check-point.

Abstract: The disclosure is directed to receiving a digitized content item that is indicated to be shared with users (e.g., all, some, or one) of a social networking system; selecting, based on one or more characteristics of the digitized content item, a second server computing device at which to further compute or store the digitized content item; and transmitting, to the second computing device, the digitized content item for storage at the second computing device. At least one of the characteristics can be a classification of a user who transmitted the digitized content item via the data communications network. By selecting second server computing devices based on characteristics, data communications network performance can be improved.

Abstract: A system and method for multi-channel communication with dual in-line memory modules (“DIMMs”) is disclosed. The system retrieves information characterizing a plurality of memory channels, each of each is configurable to facilitate data communication between a DIMM and a memory controller with associated memory channel interfaces. Based on the retrieved information, one of the memory channels is designated as the active memory channel, granting the designated memory channel the ability to issue memory requests or transactions to the DIMM. On a periodic or as-needed basis (e.g., when the active memory channel is stalled or nearly stalled), the system determines whether to designate a different of the memory channels as the active memory channel, thereby enabling the newly-designated active memory channel the ability to issue memory requests or transactions to the DIMM. In some embodiments, only one of the memory channels is active at a time for communication with each DIMM.