Real-time AI research: Difference between revisions
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Javier Gonzalez | Javier Gonzalez, [[Amazon]] <gojav@amazon.co.uk> | ||
Machine Learning researchers often try multiple configurations of optimisation libraries using libraries such as GPyOpt, without knowing which will converge best. In principle, the convergence can be visualised in real time, while different configurations or algorithm versions run in the cloud at the same time. Your task is to create a control panel with architecture support that will allow researchers to monitor large numbers of simultaneous Python experiments, interactively shutting some down, or tweaking parameters via the user interface, in response to what they see. | Machine Learning researchers often try multiple configurations of optimisation libraries using libraries such as GPyOpt, without knowing which will converge best. In principle, the convergence can be visualised in real time, while different configurations or algorithm versions run in the cloud at the same time. Your task is to create a control panel with architecture support that will allow researchers to monitor large numbers of simultaneous Python experiments, interactively shutting some down, or tweaking parameters via the user interface, in response to what they see. | ||
Revision as of 07:33, 21 October 2017
Javier Gonzalez, Amazon <gojav@amazon.co.uk>
Machine Learning researchers often try multiple configurations of optimisation libraries using libraries such as GPyOpt, without knowing which will converge best. In principle, the convergence can be visualised in real time, while different configurations or algorithm versions run in the cloud at the same time. Your task is to create a control panel with architecture support that will allow researchers to monitor large numbers of simultaneous Python experiments, interactively shutting some down, or tweaking parameters via the user interface, in response to what they see.
