Yale astronomy researcher Aritra Ghosh spent more than three months manually building a deep learning classifier for galaxy images. When experimenting with Modulos AutoML, he was able to automatically build a similar performance deep learning classifier with two weeks of computational time.
The Swiss government has published its guidelines for the use of AI within the federal government. We at Modulos support this effort.
In the traditional data science process, a team of data scientists and machine learning engineers is required. They will spend days, weeks, or even months on iteratively choosing AI models, tuning them, and testing them. Often, this tedious process is guided by the team’s experience with models and techniques and is colored by their experience and education.
The idea which led to the foundation of Modulos was a collaboration between the co-founders, Ce Zhang, a computer science professor, and Kevin Schawinski, then an astrophysicist. Both of us were doing research at ETH Zurich and we were trying to see how we could use AI to better analyze and understand astrophysics data.
The first thing you learn as an engineer is to not reinvent the wheel, so the best way to do my job is to just have our automated machine learning system deliver me a package I can plug in and it works.