Machine learning in scientific workflows

I will describe our contributions to scientific ML workflow building and optimization, which we have carried out within the Paris-Saclay Center for Data Science. I will start by mapping out the different use cases of machine learning in sciences (data collection, inference, simulation, hypothesis generation). Then I will detail some of the particular challenges of ML/science collaborations and the solutions we built to solve these challenges. I will briefly describe the open code submission RAMP tool that we built for collaborative prototyping, detail some of the workflows (e.g., the Higgs boson discovery pipeline, El Nino forecasting, detecting Mars craters on satellite images), and present results on rapidly optimizing machine learning solutions.