Abstract: A computer-implemented method for ML platform-agnostic machine learning (ML) training, the method comprising: providing a set of adapters to map an ML platform agnostic format to a plurality of ML platform specific formats; receiving, at a labeling platform, a use case associated with a use case type, the use case comprising configuration information for an ML model, the configuration information formatted according to the ML platform agnostic format; using an adapter, from the set of adapters, that corresponds to the selected ML platform, mapping the configuration information from the ML platform agnostic format to an ML platform specific format of the selected ML platform; and training the ML model using the selected ML platform and a set of training data.

Abstract: Systems, methods, and computer program products for agnostic machine learning inference are provided. A computer system, such as a labeling platform, uses a machine learning (ML) model inference configuration to configure an inference environment to use an ML model to process labeling requests. The ML model inference configuration is agnostic to the particulars of the ML platform being used. An adapter maps the ML model inference configuration to an ML platform specific format to instantiate a running system on a given platform.

Abstract: A computer system implements a file system migration by determining a destination namespace of a destination file system based at least in part on a namespace of a source file system. At least a portion of the destination namespace includes an organization that is altered as compared to a corresponding portion of the source namespace.

Abstract: One embodiment includes a computer-implemented data labeling platform. The platform provides a confidence-driven workflow (CDW) executable to receive and process labeling requests to label data items. The CDW comprises a set of executable labelers, each labeler in having a dynamically modeled confidence range. The execution path for processing a labeling request to label a data item is dynamically determined.

Abstract: Systems, methods and products for optimization of a machine learning labeler. In one method, labeling requests are received and corresponding label inferences are generated using a champion model. A portion of the labeling requests and corresponding inferences is selected for use as training data, and labels are generated for the selected requests, thereby producing corresponding augmented results. A first portion of the augmented results are provided as training data to an experiment coordinator, which then trains one or more challenger models using these augmented results. A second portion of the augmented results is provided as evaluation data to a model evaluator, which evaluates the performance of the challenger models and the champion model. If one of the challenger models has higher performance than the champion model, the model evaluator promotes the challenger model to replace the champion model.

Abstract: A migration plan is created that is based at least in part on an operator input. The resources of a destination file system are provisioned based on the migration plan. One or more processes to migrate the source file system for the provisioned resources of the destination file system are then configured based on the migration plan.

Abstract: One embodiment includes a computer-implemented data labeling platform. The platform provides a confidence-driven workflow (CDW) executable to receive and process labeling requests to label data items. The CDW comprises a set of executable labelers, each labeler in having a dynamically modeled confidence range. The execution path for processing a labeling request to label a data item is dynamically determined.

Abstract: A computer system implements a file system migration by determining a destination namespace of a destination file system based at least in part on a namespace of a source file system. At least a portion of the destination namespace includes an organization that is altered as compared to a corresponding portion of the source namespace.

Abstract: A migration plan is created that is based at least in part on an operator input. The resources of a destination file system are provisioned based on the migration plan. One or more processes to migrate the source file system for the provisioned resources of the destination file system are then configured based on the migration plan.

Abstract: A computer system implements a file system migration by determining a destination namespace of a destination file system based at least in part on a namespace of a source file system. At least a portion of the destination namespace includes an organization that is altered as compared to a corresponding portion of the source namespace.

Abstract: A server computer. The server computer comprises a processor, a non-transitory memory, a application comprising JavaScript instructions stored in the non-transitory memory, a runtime stored in the non-transitory memory, and a native agent module stored in the non-transitory memory. When executed by the processor, the runtime provides a JavaScript execution environment for executing the application and an instrumentation application programming interface (API). When executed by the processor outside of the runtime, the native agent module monitors memory buffers allocated to the application based on accessing the instrumentation API of the runtime, executes an event loop that sends an interrupt to the runtime, and provides reporting based on monitoring the memory buffers and the interrupt sent to the runtime to a management layer external to the server computer.

Abstract: A server computer. The server computer comprises a processor, a non-transitory memory, a application comprising JavaScript instructions stored in the non-transitory memory, a runtime stored in the non-transitory memory, and a native agent module stored in the non-transitory memory. When executed by the processor, the runtime provides a JavaScript execution environment for executing the application and an instrumentation application programming interface (API). When executed by the processor outside of the runtime, the native agent module monitors memory buffers allocated to the application based on accessing the instrumentation API of the runtime, executes an event loop that sends an interrupt to the runtime, and provides reporting based on monitoring the memory buffers and the interrupt sent to the runtime to a management layer external to the server computer.

Abstract: A computer system implements a file system migration by determining a destination namespace of a destination file system based at least in part on a namespace of a source file system. At least a portion of the destination namespace includes an organization that is altered as compared to a corresponding portion of the source namespace.

Abstract: A migration plan is used to create a namespace for the destination file system based at least in part on a namespace of the source file system. The migration plan is also used to determine a destination collection of policies for implementation on the destination file system. At least one of the namespace or the collection of destination policies include an operator-specific configuration that is specified by the one or more rules or parameters of the operator input.

Abstract: A migration plan is created that is based at least in part on an operator input. The resources of a destination file system are provisioned based on the migration plan. One or more processes to migrate the source file system for the provisioned resources of the destination file system are then configured based on the migration plan.

Abstract: One or more techniques and/or systems are disclosed that provide for improved speed and efficiency when transferring data from a source data storage device to a destination data storage device in a data storage network. A data storage network is configured such that a source data storage device, a destination data storage device and a migration server can communicate with each other. Both a source data storage drive and a destination data storage drive are mapped to the migration server. The migration server identifies controllers coupled to the data storage network for the source and destination data storage drives. A route of data transfer is mapped from the source data storage drive to the destination data storage drive through the identified controllers, and the data is transferred from the source data storage drive to the destination data storage drive using the mapped route of data transfer.

Abstract: Method and system for generating a migration plan for migrating information from a source storage location to a destination storage location, where storage space at the source storage location is presented as a plurality of storage volumes to a plurality of computing systems is provided. A parsed data structure is generated for a processor executable planning module used for generating the migration plan. The parsed data structure stores information regarding a plurality of computing systems that access the storage volumes for storing information at a storage device managed by a storage controller; information regarding a plurality of adapters used by the plurality of computing systems and the storage controller; and information regarding logical unit numbers (LUNs) that are presented for storing the information at the storage device.