Abstract: A method can include receiving, at a workflow controller, a machine learning workflow, the machine learning workflow associated with a first task and a second task. The first task is training a machine learning model and the second task is deploying the model. The method can include segmenting, by the workflow controller, the machine learning workflow into a first sub-workflow associated with the first task and a second sub-workflow associated with the second task, assigning a first workflow agent to the first sub-workflow and assigning a second workflow agent to the second sub-workflow, selecting, by the first workflow agent and based on first resources needed to perform the first task, a first cluster for performing the first task and selecting, by the second workflow agent and based on second resources needed to perform the second task, a second cluster for performing the second task.

Abstract: In some embodiments, a method includes processing, by a machine learning model, a natural-language expression to generate one or more rules, each rule including a trigger and one or more actions; monitoring a deployed infrastructure to detect an occurrence of at least one of the one or more triggers of a first rule of the one or more rules; and performing at least one of the one or more actions of the first rule based on the occurrence of at least one of the one or more triggers.

Abstract: In one embodiment, a method for FPGA accelerated serverless computing comprises receiving, from a user, a definition of a serverless computing task comprising one or more functions to be executed. A task scheduler performs an initial placement of the serverless computing task to a first host determined to be a first optimal host for executing the serverless computing task. The task scheduler determines a supplemental placement of a first function to a second host determined to be a second optimal host for accelerating execution of the first function, wherein the first function is not able to accelerated by one or more FPGAs in the first host. The serverless computing task is executed on the first host and the second host according to the initial placement and the supplemental placement.

Abstract: In one embodiment, a method for FPGA accelerated serverless computing comprises receiving, from a user, a definition of a serverless computing task comprising one or more functions to be executed. A task scheduler performs an initial placement of the serverless computing task to a first host determined to be a first optimal host for executing the serverless computing task. The task scheduler determines a supplemental placement of a first function to a second host determined to be a second optimal host for accelerating execution of the first function, wherein the first function is not able to accelerated by one or more FPGAs in the first host. The serverless computing task is executed on the first host and the second host according to the initial placement and the supplemental placement.

Abstract: Anomaly detection includes receiving, for one or more data points of a data set, an anomaly label indicating whether the one or more data points is an anomaly; generating, using a first machine learning model, one or more hyperparameters based on the one or more data points and the anomaly label; and training a second machine learning model to determine anomalies in the data set, wherein the training is based on the one or more hyperparameters.

Abstract: An illustrative embodiment disclosed herein is an apparatus including a processor having programmed instructions to place a first compute resource in a storage node of an object storage platform and to place a second compute resource in a compute node in a client coupled to the object storage platform via a public network. In some embodiments, unstructured data is stored in the storage node. In some embodiments, the first compute resource of the storage node preprocesses the unstructured data. In some embodiments, the preprocessed unstructured data is sent to the compute node. In some embodiments, the second compute resource trains a machine learning (ML) model using the preprocessed unstructured data.

Abstract: The present disclosure provides systems, methods and computer-readable media for optimizing the neural architecture search for the automated machine learning process. In one aspect, neural architecture search method including selecting a neural architecture for training as part of an automated machine learning process; collecting statistical parameters on individual nodes of the neural architecture during the training; determining, based on the statistical parameters, active nodes of the neural architecture to form a candidate neural architecture; and validating the candidate neural architecture to produce a trained neural architecture to be used in implemented an application or a service.

Abstract: In some embodiments, a method includes receiving a plurality of actions associated with a reinforcement learning model; generating a plurality of combinations of actions based on the plurality of actions; analyzing the plurality of combinations of actions; generating at least one subset of indispensable actions based on the analyzing; selecting a set of training actions from the plurality of actions based on the at least one subset of indispensable actions; and training the reinforcement learning model based on the set of training actions.

Abstract: A method for accelerating data operations across a plurality of nodes of one or more clusters of a distributed computing environment. Rack awareness information characterizing the plurality of nodes is retrieved and a non-volatile memory (NVM) capability of each node is determined. A write operation is received at a management node of the plurality of nodes and one or more of the rack awareness information and the NVM capability of the plurality of nodes are analyzed to select one or more nodes to receive at least a portion of the write operation, wherein at least one of the selected nodes has an NVM capability. A multicast group for the write operation is then generated wherein the selected nodes are subscribers of the multicast group, and the multicast group is used to perform hardware accelerated read or write operations at one or more of the selected nodes.

Abstract: A method for data provisioning a serverless computing cluster. A plurality of user defined functions (UDFs) are received for execution on worker nodes of the serverless computing cluster. For a first UDF, one or more data locations of UDF data needed to execute the first UDF are determined. At a master node of the serverless computing cluster, a plurality of worker node tickets are received, each ticket indicating a resource availability of a corresponding worker node. The one or more data locations and the plurality of worker node tickets are analyzed to determine eligible worker nodes capable of executing the first UDF. The master node transmits a pre-fetch command to one or more of the eligible worker nodes, causing the eligible worker nodes to become a provisioned worker node for the first UDF by storing a pre-fetched first UDF data before the first UDF is assigned for execution.

Abstract: Systems, methods, and computer-readable media for managing storing of data in a data storage system using a client tag. In some examples, a first portion of a data load as part of a transaction and a client identifier that uniquely identifies a client is received from the client at a data storage system. The transaction can be tagged with a client tag including the client identifier and the first portion of the data load can be stored in storage at the data storage system. A first log entry including the client tag is added to a data storage log in response to storing the first portion of the data load in the storage. The first log entry is then written from the data storage log to a persistent storage log in persistent memory which is used to track progress of storing the data load in the storage.

Abstract: Systems, methods, and computer-readable media for managing storing of data in a data storage system using a client tag. In some examples, a first portion of a data load as part of a transaction and a client identifier that uniquely identifies a client is received from the client at a data storage system. The transaction can be tagged with a client tag including the client identifier and the first portion of the data load can be stored in storage at the data storage system. A first log entry including the client tag is added to a data storage log in response to storing the first portion of the data load in the storage. The first log entry is then written from the data storage log to a persistent storage log in persistent memory which is used to track progress of storing the data load in the storage.

Abstract: Embodiments include receiving an indication of a data storage module to be associated with a tenant of a distributed storage system, allocating a partition of a disk for data of the tenant, creating a first association between the data storage module and the disk partition, creating a second association between the data storage module and the tenant, and creating rules for the data storage module based on one or more policies configured for the tenant. Embodiments further include receiving an indication of a type of subscription model selected for the tenant, and selecting the disk partition to be allocated based, at least in part, on the subscription model selected for the tenant. More specific embodiments include generating a storage map indicating the first association between the data storage module and the disk partition and indicating the second association between the data storage module and the tenant.

Abstract: In one embodiment, a method for FPGA accelerated serverless computing comprises receiving, from a user, a definition of a serverless computing task comprising one or more functions to be executed. A task scheduler performs an initial placement of the serverless computing task to a first host determined to be a first optimal host for executing the serverless computing task. The task scheduler determines a supplemental placement of a first function to a second host determined to be a second optimal host for accelerating execution of the first function, wherein the first function is not able to accelerated by one or more FPGAs in the first host. The serverless computing task is executed on the first host and the second host according to the initial placement and the supplemental placement.

Abstract: In one embodiment, a method for FPGA accelerated serverless computing comprises receiving, from a user, a definition of a serverless computing task comprising one or more functions to be executed. A task scheduler performs an initial placement of the serverless computing task to a first host determined to be a first optimal host for executing the serverless computing task. The task scheduler determines a supplemental placement of a first function to a second host determined to be a second optimal host for accelerating execution of the first function, wherein the first function is not able to accelerated by one or more FPGAs in the first host. The serverless computing task is executed on the first host and the second host according to the initial placement and the supplemental placement.

Abstract: A method for ranking detected anomalies is disclosed. The method includes generating a graph based on a plurality of rules, wherein the graph comprises nodes representing metrics identified in the rules, edges connecting nodes where metrics associated with connected nodes are identified in a given rule, and edge weights of the edges each representing a severity level assigned to the given rule. The method further includes ranking nodes of the graph based on the edge weights. The method further includes ranking detected anomalies based on the ranking of the nodes corresponding to the metrics associated with the detected anomalies.

Abstract: The present disclosure is directed to system and methods for providing machine learning tools such as Kubeflow and other similar ML platforms with human-in-the-loop capabilities for optimizing the resulting machine models. In one aspect, a machine learning integration tool includes memory having computer-readable instructions stored therein and one or more processors configured to execute the computer-readable instructions to execute a workflow associated with a machine learning process; determine, during execution of the machine learning process, that non-automated feedback is required; generate a virtual input unit for receiving the non-automated feedback; modify raw data used for the machine learning process with the non-automated feedback to yield updated data; and complete the machine learning process using the updated data.

Abstract: A method for ranking detected anomalies is disclosed. The method includes generating a graph based on a plurality of rules, wherein the graph comprises nodes representing metrics identified in the rules, edges connecting nodes where metrics associated with connected nodes are identified in a given rule, and edge weights of the edges each representing a severity level assigned to the given rule. The method further includes ranking nodes of the graph based on the edge weights. The method further includes ranking detected anomalies based on the ranking of the nodes corresponding to the metrics associated with the detected anomalies.

Abstract: In one embodiment, a method for FPGA accelerated serverless computing comprises receiving, from a user, a definition of a serverless computing task comprising one or more functions to be executed. A task scheduler performs an initial placement of the serverless computing task to a first host determined to be a first optimal host for executing the serverless computing task. The task scheduler determines a supplemental placement of a first function to a second host determined to be a second optimal host for accelerating execution of the first function, wherein the first function is not able to accelerated by one or more FPGAs in the first host. The serverless computing task is executed on the first host and the second host according to the initial placement and the supplemental placement.

Abstract: A method can include receiving, at a workflow controller, a machine learning workflow, the machine learning workflow associated with a first task and a second task. The first task is training a machine learning model and the second task is deploying the model. The method can include segmenting, by the workflow controller, the machine learning workflow into a first sub-workflow associated with the first task and a second sub-workflow associated with the second task, assigning a first workflow agent to the first sub-workflow and assigning a second workflow agent to the second sub-workflow, selecting, by the first workflow agent and based on first resources needed to perform the first task, a first cluster for performing the first task and selecting, by the second workflow agent and based on second resources needed to perform the second task, a second cluster for performing the second task.