Abstract: Events from one or more primary systems associated with one or more tenants are received. The received events are stored in a message queue. At least a portion of the events in the message queue are ingested for organization and storage in a data store. One or more progress identifiers associated with ingesting of the events in the message queue are tracked. An event query is received from an external system. The event query is rewritten into a first component query for the data store and a second component query for the message queue based at least in part on a progress identifier. A result of the first component query and a result of the second component query are combined to determine a result of the event query.

Abstract: Devices and techniques are generally described for confidence score generation for label generation. In some examples, first data may be received from a first computing device. In various further examples, first label data classifying at least one aspect of the first data may be received. First metadata associated with how the first label data was generated may be received. In some cases, the first label data may be generated by a first user. In various examples, a first machine learning model may generate a first confidence score associated with the first label data based at least in part on the first data and second data related to label generation by the first person. In various examples, output data comprising the first confidence score may be sent to the first computing device.

Abstract: Embodiments of technologies for machine learning-assisted content moderation include receiving a first electronic communication from a user device, where the first electronic communication identifies a content item. At least one trained machine learning model is selected from a set of trained machine learning models based on the content item and the first electronic communication. The selected trained machine learning model(s) are applied to the identified content item. The selected trained machine learning model(s) identify sub-items of the content item, corresponding content labels, and associated confidence metrics. Using the confidence metrics and one or more additional electronic communications received from the user device, a content moderation process is applied to the content item and/or the sub-items.

Abstract: Described herein are systems and methods for generating an embedding—a learned representation—for an image. The embedding for the image is derived to capture visual aspects, as well as textual aspects, of the image. An encoder-decoder is trained to generate the visual representation of the image. An optical character recognition (OCR) algorithm is used to identify text/words in the image. From these words, an embedding is derived by performing an average pooling operation on pre-trained embeddings that map to the identified words. Finally, the embedding representing the visual aspects of the image is combined with the embedding representing the textual aspects of the image to generate a final embedding for the image.

Abstract: Application-level data in a storage system are evaluated. For example, a backup analysis tool retrieves a backup object stored in the storage system. The backup analysis tool reconstructs an application object from the backup object. The backup analysis tool accesses the first application object according to a native application format associated with the first object.

Abstract: Contents of a full snapshot for storage in one or more cloud storage volumes are received. The contents of the full snapshot is stored in the one or more cloud storage volumes. A snapshot of a virtual machine data volume and a snapshot of a virtual machine boot volume are generated based on the contents of the full snapshot stored in the one or more cloud storage volumes. An image of the virtual machine boot volume is generated based on the snapshot of the virtual machine boot volume. The snapshot of the virtual machine data volume, the snapshot of the virtual machine boot volume, and the image of the virtual machine boot volume are stored in a cloud object storage.

Abstract: An instruction to generate a cloud instantiation of a secondary storage system is provided. One or more secondary storage clusters are virtually rebuilt in the cloud instantiation of the secondary storage system. A new cloud instance of a user virtual machine is deployed based on at least a portion of data stored in the one or more rebuilt secondary storage clusters of the cloud instantiation of the secondary storage system. A version of at least the portion of the data of the one or more rebuilt secondary storage clusters is provided to a cloud deployment server.

Abstract: Contents of a virtual machine of a primary storage are received at a first point in time. A full snapshot of the virtual machine is stored on a secondary storage using the received contents of the virtual machine. One or more cloud storage volumes of the virtual machine are caused to be created. Contents of the full snapshot for storage on the one or more cloud storage volumes are provided from the secondary storage. Changed data of the virtual machine of the primary storage is received at a second point in time. An incremental snapshot of the virtual machine is stored using the received changed data. The one or more cloud storage volumes are updated using the incremental snapshot of the virtual machine. An image of at least one of the cloud storage volumes is caused to be created for standby deployment of another instance of the virtual machine.

Abstract: Described herein are techniques for using supervised machine learning to determine whether a content posting posted to a feed of an online service, has been posted with the intent that the content posting is a poll or survey. Upon making a determination that a content posting is or includes a poll, the content posting is further analyzed to identify within the content posting a question and/or answers to the question. The identified question and answers are then used to populate data fields associated with a formal or structured poll, and the end-user who posted the content posting is provided an option to convert the poll from a first content posting format to a second content posting format that is specifically for a formal or structured poll.

Abstract: Application-level data in a storage system are evaluated. For example, a backup analysis tool retrieves a backup object stored in the storage system. The backup analysis tool reconstructs an application object from the backup object. The backup analysis tool accesses the first application object according to a native application format associated with the first object.

Abstract: An instruction to generate a cloud instantiation of a secondary storage system is provided. One or more secondary storage clusters are virtually rebuilt in the cloud instantiation of the secondary storage system. A new cloud instance of a user virtual machine is deployed based on at least a portion of data stored in the one or more rebuilt secondary storage clusters of the cloud instantiation of the secondary storage system. A version of at least the portion of the data of the one or more rebuilt secondary storage clusters is provided to a cloud deployment server.

Abstract: Described herein are systems and methods for generating an embedding—a learned representation—for an image. The embedding for the image is derived to capture visual aspects, as well as textual aspects, of the image. An encoder-decoder is trained to generate the visual representation of the image. An optical character recognition (OCR) algorithm is used to identify text/words in the image. From these words, an embedding is derived by performing an average pooling operation on pre-trained embeddings that map to the identified words. Finally, the embedding representing the visual aspects of the image is combined with the embedding representing the textual aspects of the image to generate a final embedding for the image.

Abstract: Application-level data in a storage system are evaluated. For example, a backup analysis tool retrieves a backup object stored in the storage system. The backup analysis tool reconstructs an application object from the backup object. The backup analysis tool accesses the first application object according to a native application format associated with the first object.

Abstract: Described herein are techniques for using supervised machine learning to determine whether an image that has been posted to a feed of an online service includes a quotation. In some instance, supervised machine learning techniques are used to infer or predict an intent of a content poster in posting a content item to a feed of an online service. By better understanding the nature of the content being posted, various recommendations can be made during the time when an end-user is posting content, and thereafter.

Abstract: An instruction to generate a cloud instantiation of a secondary storage system is provided. One or more secondary storage clusters are virtually rebuilt in the cloud instantiation of the secondary storage system. A new cloud instance of a user virtual machine is deployed based on at least a portion of data stored in the one or more rebuilt secondary storage clusters of the cloud instantiation of the secondary storage system. A version of at least the portion of the data of the one or more rebuilt secondary storage clusters is provided to a cloud deployment server.

Abstract: A processor may launch a common layer including a browser and a shell. The processor may identify a request for data required by the software platform. The processor may determine a context in which the request was generated by detecting, by the shell, whether the software platform is an on-premise solution or a cloud solution; examining, by the shell, the request to determine whether it requires local resources or cloud-based resources; and designating, by the shell, the context as a cloud-based context or an on-premise context based on the detecting and the examining. In response to the determining, the processor may perform processing related to a request generated in the on-premise context using at least one local resource stored in a local data store; and communicate, by the browser, with at least one cloud-based resource to perform processing related to a request generated in the cloud-based context.

Abstract: Contents of a virtual machine of a primary storage are received at a first point in time. A full snapshot of the virtual machine is stored on a secondary storage using the received contents of the virtual machine. One or more cloud storage volumes of the virtual machine are caused to be created. Contents of the full snapshot for storage on the one or more cloud storage volumes are provided from the secondary storage. Changed data of the virtual machine of the primary storage is received at a second point in time. An incremental snapshot of the virtual machine is stored using the received changed data. The one or more cloud storage volumes are updated using the incremental snapshot of the virtual machine. An image of at least one of the cloud storage volumes is caused to be created for standby deployment of another instance of the virtual machine.

Abstract: Techniques are disclosed for managing a high performance, fault-tolerant, strongly consistent, distributed key-value store system. The key-value store may store information, such as metadata for a distributed file system. Fault-tolerance means that the distributed key-value store continues to provide access to values in the key-value store in spite of a certain number of node failures. To provide this capability, the key-value store may store copies of (key, value) pair on N+1 nodes in order to provide fault tolerance for the failure of up to N nodes. In addition, metadata describing which nodes store a given value is stored on 2N+1 nodes and the distributed key-value store is sized such that there are 3N+1 nodes in a cluster. Doing so allows the key, value store to tolerate a failure of N nodes, while still maintaining a consistent and available key-value store.

Abstract: Contents of a virtual machine of a primary storage are received at a first point in time. A full snapshot of the virtual machine is stored on a secondary storage using the received contents of the virtual machine. One or more cloud storage volumes of the virtual machine are caused to be created. Contents of the full snapshot for storage on the one or more cloud storage volumes are provided from the secondary storage. Changed data of the virtual machine of the primary storage is received at a second point in time. An incremental snapshot of the virtual machine is stored using the received changed data. The one or more cloud storage volumes are updated using the incremental snapshot of the virtual machine. An image of at least one of the cloud storage volumes is caused to be created for standby deployment of another instance of the virtual machine.

Abstract: Application-level data in a storage system are evaluated. For example, a backup analysis tool retrieves a backup object stored in the storage system. The backup analysis tool reconstructs an application object from the backup object. The backup analysis tool accesses the first application object according to a native application format associated with the first object.