Abstract: An apparatus includes a processor to: train a first neural network of a chain to generate first configuration data including first trained parameters, wherein the chain performs an analytical function generating a set of output values from a set of input values, each neural network has inputs to receive the set of input values and outputs to output a portion of the set of output values, and the neural networks are ordered from the first at the head to a last neural network at the tail, and are interconnected so that each neural network additionally receives the outputs of a preceding neural network; train, using the first configuration data, a next neural network in the chain ordering to generate next configuration data including next trained parameters; and use at least the first and next configuration data and data indicating the interconnections to instantiate the chain to perform the analytical function.

Abstract: An apparatus includes a processor to: train a first neural network of a chain to generate first configuration data including first trained parameters, wherein the chain performs an analytical function generating a set of output values from a set of input values, each neural network has inputs to receive the set of input values and outputs to output a portion of the set of output values, and the neural networks are ordered from the first at the head to a last neural network at the tail, and are interconnected so that each neural network additionally receives the outputs of a preceding neural network; train, using the first configuration data, a next neural network in the chain ordering to generate next configuration data including next trained parameters; and use at least the first and next configuration data and data indicating the interconnections to instantiate the chain to perform the analytical function.

Abstract: An apparatus includes a processor to: train a first neural network of a chain to generate first configuration data including first trained parameters, wherein the chain performs an analytical function generating a set of output values from a set of input values, each neural network has inputs to receive the set of input values and outputs to output a portion of the set of output values, and the neural networks are ordered from the first at the head to a last neural network at the tail, and are interconnected so that each neural network additionally receives the outputs of a preceding neural network; train, using the first configuration data, a next neural network in the chain ordering to generate next configuration data including next trained parameters; and use at least the first and next configuration data and data indicating the interconnections to instantiate the chain to perform the analytical function.

Abstract: An apparatus includes a processor to: train a first neural network of a chain to generate first configuration data including first trained parameters, wherein the chain performs an analytical function generating a set of output values from a set of input values, each neural network has inputs to receive the set of input values and outputs to output a portion of the set of output values, and the neural networks are ordered from the first at the head to a last neural network at the tail, and are interconnected so that each neural network additionally receives the outputs of a preceding neural network; train, using the first configuration data, a next neural network in the chain ordering to generate next configuration data including next trained parameters; and use at least the first and next configuration data and data indicating the interconnections to instantiate the chain to perform the analytical function.

Abstract: An apparatus includes a processor to: receive a request to generate a DAG of a job flow of multiple tasks of an analysis based on data table(s) and formulae of a spreadsheet data structure; correlate each indication of data required as input or output to at least a subpart of a data table; identify data dependencies and determine an order of performance among the multiple tasks based on the formulae; generate, within the specified federated area, a job flow definition that specifies the order of performance of the multiple tasks; for each task of the multiple tasks, generate, within the specified federated area, a corresponding macro data structure of multiple macro data structures; and generate the requested visualization based on the job flow definition and the multiple macro data structures.

Abstract: An apparatus includes a processor to: receive a request to provide, within a specified federated area, a set of objects that enable a performance of a job flow to perform multiple tasks of an analysis based on data table(s) and formulae of a spreadsheet data structure, wherein the set of objects includes at least one task routine to perform a task of the multiple tasks; correlate each indication of data required as input or output to at least a subpart of a data table; identify data dependencies and determine an order of performance among the multiple tasks based on the formulae; generate, within the specified federated area, a job flow definition that specifies the order of performance of the multiple tasks; and for each task routine of the at least one task routine, generate, within the specified federated area, a corresponding macro data structure.

Abstract: An apparatus includes a processor to: perform a testing job flow at least partly within a testing federated area to test a neural network defined by configuration data specifying hyperparameters and trained parameters thereof; and perform a transfer flow to transfer an object indicative of results of the testing from the testing federated area to another federated area, wherein: in response to the degree of accuracy falling below a predetermined minimum threshold, the processor is caused to transfer a specification of the degree of accuracy or a portion of inaccurate output to a training federated area in which the neural network was at least partly trained; and in response to the degree of accuracy exceeding a predetermined maximum threshold, the processor is caused to transfer a copy of the neural network configuration data to a usage federated area in which the neural network is to be made available for use.

Abstract: An apparatus includes a processor to: assign a portion of currently available instruction-based processing resources to a first non-neuromorphic performance of an analytical function; in response to availability of sufficient remaining processing resources for a first neuromorphic performance of the analytical function with the same input values, assign a portion of the remaining processing resources to the first neuromorphic performance; analyze the output values generated by the first neuromorphic and non-neuromorphic performances to determine a degree of accuracy of the neural network in performing the analytical function; in response to at least the degree of accuracy exceeding a predetermined threshold, assign a portion of currently available processing resources to a second neuromorphic performance of the analytical function; and in response to availability of sufficient remaining processing resources for a second non-neuromorphic performance of the analytical function, assign a portion of the remaining

Abstract: An apparatus includes a processor to: receive a request to repeat an earlier performance of a first job flow described in a job flow definition; analyze the job flow definition to determine whether the first job flow uses a neural network; in response to a determination that the first job flow uses a neural network, analyze an object associated with the first job flow to determine whether the neural network was trained using training data from a second job flow that does not use a neural network; and in response to a determination that such training data was so used, repeat the earlier performance of the first job flow, perform the second job flow with the same input data values as used in the repeated performance of the first job flow, and analyze corresponding output data values of both performances to determine a degree of accuracy of the neural network.

Abstract: An apparatus includes a processor to: receive a request to generate a DAG of a job flow of multiple tasks of an analysis based on data table(s) and formulae of a spreadsheet data structure; correlate each indication of data required as input or output to at least a subpart of a data table; identify data dependencies and determine an order of performance among the multiple tasks based on the formulae; generate, within the specified federated area, a job flow definition that specifies the order of performance of the multiple tasks; for each task of the multiple tasks, generate, within the specified federated area, a corresponding macro data structure of multiple macro data structures; and generate the requested visualization based on the job flow definition and the multiple macro data structures.

Abstract: An apparatus includes a processor to: receive a request to provide, within a specified federated area, a set of objects that enable a performance of a job flow to perform multiple tasks of an analysis based on data table(s) and formulae of a spreadsheet data structure, wherein the set of objects includes at least one task routine to perform a task of the multiple tasks; correlate each indication of data required as input or output to at least a subpart of a data table; identify data dependencies and determine an order of performance among the multiple tasks based on the formulae; generate, within the specified federated area, a job flow definition that specifies the order of performance of the multiple tasks; and for each task routine of the at least one task routine, generate, within the specified federated area, a corresponding macro data structure.

Abstract: An apparatus includes a processor to: assign a portion of currently available instruction-based processing resources to a first non-neuromorphic performance of an analytical function; in response to availability of sufficient remaining processing resources for a first neuromorphic performance of the analytical function with the same input values, assign a portion of the remaining processing resources to the first neuromorphic performance; analyze the output values generated by the first neuromorphic and non-neuromorphic performances to determine a degree of accuracy of the neural network in performing the analytical function; in response to at least the degree of accuracy exceeding a predetermined threshold, assign a portion of currently available processing resources to a second neuromorphic performance of the analytical function; and in response to availability of sufficient remaining processing resources for a second non-neuromorphic performance of the analytical function, assign a portion of the remaining

Abstract: An apparatus includes a processor to: receive a request to repeat an earlier performance of a first job flow described in a job flow definition; analyze the job flow definition to determine whether the first job flow uses a neural network; in response to a determination that the first job flow uses a neural network, analyze an object associated with the first job flow to determine whether the neural network was trained using training data from a second job flow that does not use a neural network; and in response to a determination that such training data was so used, repeat the earlier performance of the first job flow, perform the second job flow with the same input data values as used in the repeated performance of the first job flow, and analyze corresponding output data values of both performances to determine a degree of accuracy of the neural network.

Abstract: An apparatus includes a processor to: perform a testing job flow at least partly within a testing federated area to test a neural network defined by configuration data specifying hyperparameters and trained parameters thereof; and perform a transfer flow to transfer an object indicative of results of the testing from the testing federated area to another federated area, wherein: in response to the degree of accuracy falling below a predetermined minimum threshold, the processor is caused to transfer a specification of the degree of accuracy or a portion of inaccurate output to a training federated area in which the neural network was at least partly trained; and in response to the degree of accuracy exceeding a predetermined maximum threshold, the processor is caused to transfer a copy of the neural network configuration data to a usage federated area in which the neural network is to be made available for use.

Abstract: An apparatus includes a processor to: receive, from a first remote device, a request to perform at least one iteration of a first job flow at least partly within a first federated area, wherein access to the first federated area is granted to the first remote device and not a second remote device, access to a second federated area is granted to the second remote device and not the first remote device, and a transfer area is maintained to transfer an object between the first and second federated areas; perform the at least one iteration of the first job flow; and analyze an output object generated in each iteration to determine whether a condition has been met to transfer an object from the first federated area to the transfer area to enable its transfer to the second federated area to enable its use in a second job flow.

Abstract: An apparatus includes a processor to: receive, from a first remote device, a request to perform at least one iteration of a first job flow at least partly within a first federated area, wherein access to the first federated area is granted to the first remote device and not a second remote device, access to a second federated area is granted to the second remote device and not the first remote device, and a transfer area is maintained to transfer an object between the first and second federated areas; perform the at least one iteration of the first job flow; and analyze an output object generated in each iteration to determine whether a condition has been met to transfer an object from the first federated area to the transfer area to enable its transfer to the second federated area to enable its use in a second job flow.

Abstract: An apparatus includes a processor to: receive, from a first remote device, a request to perform iterations of a training job flow to generate a neural network at least partly within a first federated area, wherein access to the first federated area is granted to the first remote device and not a second remote device, access to a second federated area is granted to the second remote device and not the first remote device, and a transfer area is maintained to transfer a neural network data set between the first and second federated areas; perform the at least some iterations; and analyze an output object generated in each iteration to determine whether a condition has been met to transfer a copy of the neural network data set from the first federated area to the transfer area to enable its transfer to the second federated area to test the neural network.

Abstract: An apparatus includes a processor to: receive, from a first remote device, a request to perform iterations of a training job flow to generate a neural network at least partly within a first federated area, wherein access to the first federated area is granted to the first remote device and not a second remote device, access to a second federated area is granted to the second remote device and not the first remote device, and a transfer area is maintained to transfer a neural network data set between the first and second federated areas; perform the at least some iterations; and analyze an output object generated in each iteration to determine whether a condition has been met to transfer a copy of the neural network data set from the first federated area to the transfer area to enable its transfer to the second federated area to test the neural network.

Abstract: An apparatus may include a processor and storage to store instructions that cause the processor to perform operations including: in response to a determination that a GPU of a node device is available, determine whether a task routine can be compiled to generate a GPU task routine for execution by the GPU to cause performance of multiple instances of a task of the task routine at least partially in parallel without dependencies thereamong; and in response to a determination that the task routine is able to be compiled to generate the GPU task routine: employ a conversion rule to convert the task routine into the GPU task routine; compile the GPU task routine for execution by the GPU; and assign performance of the task with a data set partition to the node device to enable performance of the multiple instances with the data set partition by the GPU.

Abstract: An apparatus may include a processor and storage to store instructions that cause the processor to perform operations including: in response to a determination that a GPU of a node device is available, determine whether a task routine can be compiled to generate a GPU task routine for execution by the GPU to cause performance of multiple instances of a task of the task routine at least partially in parallel without dependencies thereamong; and in response to a determination that the task routine is able to be compiled to generate the GPU task routine: employ a conversion rule to convert the task routine into the GPU task routine; compile the GPU task routine for execution by the GPU; and assign performance of the task with a data set partition to the node device to enable performance of the multiple instances with the data set partition by the GPU.