Machine learning (ML) techniques that make predictions based on data are key to the modern data-driven industry. However, SMEs usually cannot afford the necessary specialist knowledge and therefore cannot benefit from ML. In this project, we evaluated whether our open-source prototype for automated machine learning (AutoML), Auto-sklearn, has the potential to lift this barrier.
Throughout the POC project we:
* analyzed the global market of existing AutoML solutions and to what extent these solutions are relevant to companies within Germany and the EU,
* studied the demand for AutoML solutions among data scientists and ML-practitioners and identified a strong interest in AutoML,
* researched how and for what our prototype is currently used and identified potential future extensions,
* integrated research findings from the ERC grant Beyond Blackbox into our open-source AutoML toolkit to further improve it (yielding the publication “Auto-sklearn 2.0: Hands-free AutoML via Meta-Learning” by Feurer et al. (2021)), and
* studied commercialization strategies for open-source and identified potential directions for our prototype.
To conclude, our analysis revealed several promising new directions for commercializing our AutoML technology which should be pursued further.
