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Great product - I love that you can actually use it immediately. Will give it a try. Some comments below though. There's a whole ecosystem of tools now being b

Great product - I love that you can actually use it immediately. Will give it a try. Some comments below though. There's a whole ecosystem of tools now being built around explainability, bias-awareness, and more given that your models can only be as good as your datasets. I'd go as far as arguing that building models isn't the hard part, it's instead building really good datasets. I come from a computer-vision background but I think this definitely applies to all types of tabular / numerical data as well. How do you see a tool like yours playing into that realization?

halflings 5y ago
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.

halflings 5y ago

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.