Subtree Inc.

Storageos Limited

Karolis Rusenas

Luke Marsden

Alaric Blagrave Snell-Pym

Charlotte Rachael Godley

Alex Chircop

Simon Croome

A system for tracking and representing data science model runs includes a hub including a first computing device communicatively coupled with a data store. A runner including a second computing device having a cache is communicatively coupled with the hub through a telecommunications network. An end user computing device includes a display and is communicatively coupled with the runner and the hub. User interfaces displayed on the display include: a unique identifier identifying a data science model run performed by the runner; a list of input files used by the runner to perform the run; a list of output files output by the runner as a result of the run; and a diagram diagramming a process flow including a visual representation of the input files, a visual representation of the run, and a visual representation of the output files.

Systems and methods for tracking and representing data science model runs

A system for tracking and representing data science data runs includes a hub including a first computing device communicatively coupled with a data store. A runner including a second computing device having a cache is communicatively coupled with the hub through a telecommunications network. An end user computing device includes a display and is communicatively coupled with the runner and the hub. User interfaces displayed on the display include: a unique identifier identifying a data science data run performed by the runner; a list of input files used by the runner to perform the run; a list of output files output by the runner as a result of the run; and a diagram diagramming a process flow including a visual representation of the input files, a visual representation of the run, and a visual representation of the output files.

Systems and methods for tracking and representing data science data runs

A method for provisioning a volume of data is disclosed. The method involves identifying a set of rules associated with the volume of data, wherein the set of rules includes at least two conflicting rules, prioritizing the at least two conflicting rules, and placing the volume of data on a first computer system according to the prioritization. The method also includes collecting performance metrics corresponding to the placed volume of data, computing a score based on the collected performance metrics, determining if the computed score is acceptable, and determining that the placement is successful when the computer score is determined to be acceptable. The method further includes changing the prioritization of the at least two conflicting rules and placing the volume of data on a second computer system according to the changed prioritization when the score is determined to be unacceptable. The volume of data may be placed on the second computer system only during hours when the second computer system is not used, or during a period of time in which activity on the second computer system is relatively low.

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London, United Kingdom

Karolis Rusenas