Projects

Skillset

Over the last years I have designed, developed and tested prototypes with the following technologies:

ML Solution Framework

ML Solution Tester

The ML Solution Tester serves as a validation tool for data scientists and software developers to ensure that the metadata they produce can be integrated in a common metadata repository. The prototype lets different teams know which ML solutions have been contributed to the metadata repository and provides them with a code to submit their own development.

ML Solution Designer

This prototype offers an interface to design ML solution specifications based on Axiomatic Design for Machine Learning (AD4ML). The resulting ML solution specifications can be assessed by the tool before the corresponding ML solution is implemented. It also allows development teams to build a repository of reusable specification components, which could be used in the future to automatically recommend components when new ML solutions are specified.

ML Solution Viewer

The ML Solution Viewer displays the annotated metadata of ML solutions components. The metadata describes the data features used to train an ML model, the technical configuration, version numbers and parameter values of the software components used, the hardware resources required to deploy the ML solution and the performance that can be expected across multiple metrics. These metadata summarize all necessary information to enable the reproducibility of ML results. They also serve as input data for the reuse recommendations provided by AssistML.

AssistML

Assist ML recommends existing ML solutions to be reused in new use cases. The prototype finds the ML solutions that better suit the performance preferences of the new use case from those in the metadata repository. AssistML then presents the selected solutions to decision makers in simple and intuitive reports. This reduces the development time for new projects.