Automation of Data Prep, ML, and Data Science: New Cure or Snake Oil?

As machine learning (ML), artificial intelligence (AI), and Data Science grow in practical importance, a large part of the ML/AI software industry claims to have built tools and platforms to automate the entire workflow of ML. That includes vexing problems of data preparation (prep), studied intensively by the database (DB) community for decades, with basically no resolution so far. Such claims by the ML/AI industry face a stunning lack of scientific scrutiny from the DB and ML research worlds, largely due to the lack of meaningful, large, and objective benchmarks. As such tools rapidly gain adoption among enterprises and other customers, this panel will debate whether the new ML/AI industry is basically selling "snake oil" to such users, how to evolve away from the status quo by instituting meaningful new benchmarks, creating new partnerships between industry and academia for this, and other pressing questions in this important arena. We aim to spur vigorous conversations that will hopefully lead to genuine new cures for an age-old affliction in Data Science.