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I think there is a big craze with regard to ML. Most people just draw a black box call it ML/Brain/"hire data scientist" without realizing that for most nontri
by dk8996 12y ago
I think there is a big craze with regard to ML. Most people just draw a black box call it ML/Brain/"hire data scientist" without realizing that for most nontrivial problems its not going to work like magic. Some of the things that I see people underestimating is; a) how hard it is to make the magic black box b) amount of data you need c) how clean the data needs to be d) at times, you need lots of human annotated data -- cost and time to collect it e) how much it's a art than a science. Moreover, people don't have a good understating of the technical challenges and cost when you need to scale to n (features) * m (customers) * p (products). Maybe Azure solves some of these challenges but quickly looking at the pricing -- it seems like its a bit too costly.
- louisdorard 12y agoI agree that making the magic black box is very hard. Automatic model selection, at scale, requires a lot of processing power. My take is that Azure "just" makes it easier/quicker to run ML experiments, and to deploy models. It's not entirely black box since you have to pick an algorithm and parameters. I expect that once you've run your experiments and found what works best, you should be able to get similar results with an open source implementation of your chosen algorithm. But then you'd still have to deploy your model somewhere — maybe using a platform like yhathq.com which makes things more transparent?
- gomesnayagam 12y agocompletely agree,we are facing the similar problem. But Azure provide a minimum viable lab before you take next step towards ML in your company or product stack.