Abstract: 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.

Abstract: A system for large-scale machine learning experiment execution, including: a platform configured to determine an experiment set from a run specification and schedule a run to one or more clusters; and a set of agents configured to receive the experiment set from the platform and facilitate individual experiment execution through a cluster orchestrator.

Abstract: A system for large-scale machine learning experiment execution, including: a platform configured to determine an experiment set from a run specification and schedule a run to one or more clusters; and a set of agents configured to receive the experiment set from the platform and facilitate individual experiment execution through a cluster orchestrator.

Abstract: A system for large-scale machine learning experiment execution, including: a platform configured to determine an experiment set from a run specification and schedule a run to one or more clusters; and a set of agents configured to receive the experiment set from the platform and facilitate individual experiment execution through a cluster orchestrator.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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, placing the volume of data on a first computer system according to the prioritization, 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, determining that the placement is successful if the computer score is determined to be acceptable, and 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 if the score is determined to be unacceptable.

Abstract: 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.

Abstract: 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, placing the volume of data on a first computer system according to the prioritization, 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, determining that the placement is successful if the computer score is determined to be acceptable, and 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 if the score is determined to be unacceptable.