Abstract: In some implementations, a system may monitor session data associated with a first module and a second module of a platform. The system may determine a rate of communication between the first module and the second module based on the session data. The system may determine, using an optimization model, a co-location score associated with the first module and the second module based on the rate of communication, wherein the co-location score indicates an impact of co-location of the first module and the second module. The system may determine that the co-location score satisfies a co-location score threshold associated with an improvement to an operation of the platform. The system may perform an action associated with co-locating the first module and the second module.

Abstract: Methods and systems are disclosed herein for managing software operations in a computer system. A software operation may include many tasks. The tasks may be grouped together based on the tasks' dependencies on output generated from other tasks. Each group of tasks may be placed in a block of a blockchain based on the dependencies. If the output of a block fails to pass a validation test, the tasks in each block may be undone in an organized order (e.g., starting with the most recently performed task and using the one or more rollback functions associated with each task), which may prevent problems that could occur when some asynchronous tasks complete and others fail. Use of the blockchain may allow the computer system to determine more precisely where an operation failed and may allow the computer system to determine more information about the failure.

Abstract: In some implementations, a system may monitor session data associated with a first module and a second module of a platform. The system may determine a rate of communication between the first module and the second module based on the session data. The system may determine, using an optimization model, a co-location score associated with the first module and the second module based on the rate of communication, wherein the co-location score indicates an impact of co-location of the first module and the second module. The system may determine that the co-location score satisfies a co-location score threshold associated with an improvement to an operation of the platform. The system may perform an action associated with co-locating the first module and the second module.

Abstract: In some implementations, a system may monitor session data associated with a first module and a second module of a platform. The system may determine a rate of communication between the first module and the second module based on the session data. The system may determine, using an optimization model, a co-location score associated with the first module and the second module based on the rate of communication, wherein the co-location score indicates an impact of co-location of the first module and the second module. The system may determine that the co-location score satisfies a co-location score threshold associated with an improvement to an operation of the platform. The system may perform an action associated with co-locating the first module and the second module.

Abstract: Methods and systems are disclosed herein for managing software operations in a computer system. A software operation may include many tasks. The tasks may be grouped together based on the tasks' dependencies on output generated from other tasks. Each group of tasks may be placed in a block of a blockchain based on the dependencies. If the output of a block fails to pass a validation test, the tasks in each block may be undone in an organized order (e.g., starting with the most recently performed task and using the one or more rollback functions associated with each task), which may prevent problems that could occur when some asynchronous tasks complete and others fail. Use of the blockchain may allow the computer system to determine more precisely where an operation failed and may allow the computer system to determine more information about the failure.

Abstract: A data transformation system for implementing reproducible and consistent data transformations in multiple execution contexts (batch, streaming, etc.) where the transformation function/logic initially acts on historical raw data to produce derived data to train a machine learning model. When the model is trained and deployed to handle streaming event data, the same transformation is reused to transform streaming data into the appropriate derived data for the model scoring, and later for a refit of the model.

Abstract: In some implementations, a system may monitor session data associated with a first module and a second module of a platform. The system may determine a rate of communication between the first module and the second module based on the session data. The system may determine, using an optimization model, a co-location score associated with the first module and the second module based on the rate of communication, wherein the co-location score indicates an impact of co-location of the first module and the second module. The system may determine that the co-location score satisfies a co-location score threshold associated with an improvement to an operation of the platform. The system may perform an action associated with co-locating the first module and the second module.