Abstract: Apparatuses, systems, program products, and method are disclosed for machine learning abstraction. An apparatus includes an objective module configured to receive an objective to be analyzed using machine learning. An apparatus includes a grouping module configured to select a logical grouping of one or more machine learning pipelines to analyze a received objective. An apparatus includes an adjustment module configured to dynamically adjust one or more machine learning settings for a logical grouping of one or more machine learning pipelines based on feedback generated in response to analyzing a received objective.

Abstract: An automated machine learning (“ML”) method may include training a first machine learning model using a first machine learning algorithm and a training data set; validating the first machine learning model using a validation data set, wherein validating the first machine learning model comprises generating an error data set; training a second machine learning model to predict a suitability of the first machine learning model for analyzing an inference data set, wherein the second machine learning model is trained using a second machine learning algorithm and the error data set; and triggering a remedial action associated with the first or second machine learning model in response to a predicted suitability of the first machine learning model for analyzing the inference data set not satisfying a suitability threshold.

Abstract: According to an aspect, a system for stimulating muscle comprises a set of stimulators to stimulate a first muscles, a set of sensors providing first measurement comprising a set of electromyograms (EMG) from another set of muscles coupled to the body part, a set of sensors mounted in the vicinity of the set of stimulator and the set of sensors, providing a measurement representing a motion and orientation of the first body part, another set of sensors providing a measurement representing a condition external to the body part, a processor generating a first position of the body part at a first time instance from the measurements and a processor adjusting at least one of a time duration, trigger instance, and trigger strength of the set of stimulator. In that, the comparator generates a position error as difference between the first position and a reference.

Abstract: Apparatuses, systems, program products, and methods are disclosed for interpretability-based machine learning adjustment during production. An apparatus includes a first results module that is configured to receive a first set of inference results of a first machine learning algorithm during inference of a production data set. An apparatus includes a second results module that is configured to receive a second set of inference results of a second machine learning algorithm during inference of a production data set. An apparatus includes an action module that is configured to trigger one or more actions that are related to a first machine learning algorithm in response to a comparison of first and second sets of inference results not satisfying explainability criteria.

Abstract: Apparatuses, systems, program products, and methods are disclosed for determining validity of machine learning algorithms for datasets. An apparatus includes a primary training module that is configured to train a first machine learning model for a first machine learning algorithm. An apparatus includes a primary validation module that is configured to validate a first machine learning model to generate an error data set. An apparatus includes a secondary training module that is configured to train a second machine learning model for a second machine learning algorithm using an error data set. A second machine learning algorithm may be configured to predict a suitability of a first machine learning model for analyzing an inference data set. An apparatus includes an action module that is configured to trigger an action in response to a predicted suitability of the first machine learning model not satisfying a predetermined suitability threshold.

Abstract: Techniques for synchronization between data structures for original locks and mirror lock data structures are disclosed herein. The mirror lock data structures are being maintained during various scenarios including volume move and aggregate relocation, in order to preserve the non-disruptive persistent operation on storage initiated by clients. According to one embodiment, a storage node determines a plurality of data container locks to be synchronized to a partner node of the storage node and transfers metadata that indicates states of variables that represent the plurality of data container locks to the partner node in a batch. When a client initiates a data access operation that causes an attempt to modify a data container lock of the plurality of data container locks, the storage node sends a retry code to a client that prompts the client to retry the data access operation after a predetermined time period.

Abstract: Apparatuses, systems, program products, and method are disclosed for detecting suitability of machine learning models for datasets. An apparatus includes a training evaluation module configured to calculate a first statistical data signature for a training data set of a machine learning system using one or more predefined statistical algorithms. An apparatus includes an inference evaluation module configured to calculate a second statistical data signature for an inference data set of a machine learning system using one or more predefined statistical algorithms. An apparatus includes a score module configured to calculate a suitability score describing the suitability of a training data set to an inference data set as a function of a first and a second statistical data signature. An apparatus includes an action module configured to perform an action related to a machine learning system in response to a suitability score satisfying an unsuitability threshold.

Abstract: Apparatuses, systems, program products, and method are disclosed for machine learning abstraction. An apparatus includes an objective module configured to receive an objective to be analyzed using machine learning. An apparatus includes a grouping module configured to select a logical grouping of one or more machine learning pipelines to analyze a received objective. An apparatus includes an adjustment module configured to dynamically adjust one or more machine learning settings for a logical grouping of one or more machine learning pipelines based on feedback generated in response to analyzing a received objective.

Abstract: According to an aspect, a system for stimulating muscle comprises a set of stimulators to stimulate a first muscles, a set of sensors providing first measurement comprising a set of electromyograms (EMG) from another set of muscles coupled to the body part, a set of sensors mounted in the vicinity of the set of stimulator and the set of sensors, providing a measurement representing a motion and orientation of the first body part, another set of sensors providing a measurement representing a condition external to the body part, a processor generating a first position of the body part at a first time instance from the measurements and a processor adjusting at least one of a time duration, trigger instance, and trigger strength of the set of stimulator. In that, the comparator generates a position error as difference between the first position and a reference.

Abstract: Techniques for synchronization between data structures for original locks and mirror lock data structures are disclosed herein. The mirror lock data structures are being maintained during various scenarios including volume move and aggregate relocation, in order to preserve the non-disruptive persistent operation on storage initiated by clients. According to one embodiment, a storage node determines a plurality of data container locks to be synchronized to a partner node of the storage node and transfers metadata that indicates states of variables that represent the plurality of data container locks to the partner node in a batch. When a client initiates a data access operation that causes an attempt to modify a data container lock of the plurality of data container locks, the storage node sends a retry code to a client that prompts the client to retry the data access operation after a predetermined time period.

Abstract: Techniques for synchronization between data structures for original locks and mirror lock data structures are disclosed herein. The mirror lock data structures are being maintained during various scenarios including volume move and aggregate relocation, in order to preserve the non-disruptive persistent operation on storage initiated by clients. According to one embodiment, a storage node determines a plurality of data container locks to be synchronized to a partner node of the storage node and transfers metadata that indicates states of variables that represent the plurality of data container locks to the partner node in a batch. When a client initiates a data access operation that causes an attempt to modify a data container lock of the plurality of data container locks, the storage node sends a retry code to a client that prompts the client to retry the data access operation after a predetermined time period.