Christian König, Data Science Coach, Old World Computing
In the real world many projects in the domain of machine learning face problems with the deployment of the solution. In many cases there’s a limited understanding about machine learning to specify the target solution at all. Hence a data scientist needs to approach that in an agile way, which requires the ability to swiftly create end user interfaces to showcase results and make them “feelable”. After showcasing, the results need to be reusable for real deployment in order to not waste money, effort, and time.
In this presentation, Christian demonstrates a new extension that adds these abilities to the RapidMiner platform in a flexible and seamless way. RapidMiner processes are used to build the app and specify the data logic behind it.