Over the last years, the field of machine learning has substantially
changed the work in many different scientific disciplines, including the
visualization community. Based on our experience of conducting projects
at the intersection of machine learning (ML) and interactive
visualization (Vis) over the last decade, my talk will reflect on and
discuss the current relation between these two areas. For that purpose,
the talk’s structure will follow two main ideas. First, I will talk
about *Vis for ML*, that is, the idea that visualization can help
machine learning researchers and practitioners gaining interesting
insights into the models they are building. Here, I will specifically
focus on visual parameter space analysis, and illustrate how this
approach can help to better understand ML models, such as dimensionality
reduction, clustering, and classification models. In the second part, I
will turn the relationship around and discuss the contribution that *ML
for Vis* can make. While other communities seem to have been much
quicker in adopting ML pipelines, ML for Vis has gained little attention
yet, but bears the potential to automatize the visualization design
process. This new approach might potentially lead to a fundamental
paradigm shift in how visualization research and design will be done in
the future.