Thanks! Ease of use (e.g. no lengthy signups / waiting minutes for cloud jobs to schedule) is one of the main features, so I'm happy you appreciate that! RE:bi

Thanks! Ease of use (e.g. no lengthy signups / waiting minutes for cloud jobs to schedule) is one of the main features, so I'm happy you appreciate that! RE:bias/explainability, that's indeed the main argument against asking people to use ML as a black-box. I think fully understanding the very nuanced biases that can sneak in data (e.g. selectivity bias from some events being more represented in records, let's say) does require a keen sense for data, which an automated tool likely cannot provide. My bet is that this is not enough of a reason to block people from at least dipping their toe in the ML world, and that we can do more education down the line (e.g. about evaluating systems in real-life, to at least catch underperformance before looking for its source) to solve these cases. On explainability, some basic tools (e.g. feature importance) are in the roadmap, I hope to get to it in this quarter! RE:creating datasets being the hardest part of the job, not modeling... well, I think you're 100% right. And that's a tougher nut to crack. One thing I'm planning to do to help here is to provide a number of "templates", e.g. concrete use cases that people can piggy back on. e.g. explain to realtors that they can estimate house prices by creating a spreadsheet with features A,B,C and D. I can't do this for every imaginable use case, but I hope this is enough to at least inspire people on how to think about data and ML.