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Your comment is in wild and incredulous disagreement with widely published results and my own ~10 years of industry experience doing ML professionally in ecomme

by mlthoughts2018 6y ago

Your comment is in wild and incredulous disagreement with widely published results and my own ~10 years of industry experience doing ML professionally in ecommerce, quant finance, education technology and quant advertising. In fact, I’ve always found even just plain cost per unit service goes down with the introduction of more complex ML models. Their greater training complexity and compute costs are much more than amortized by improved performance, easier ability to train and deploy new models (it’s much harder and labor intensive to adjust a rat’s nest of custom business rules than a black box ML model, even in terms of transparency). Just reduction of operating costs alone is usually a reason to favor ML solutions, even if they only achieve parity with rules systems (though usually they outperform them by a lot). Your comment makes me feel your methodology for assessing business value and comparing with rule systems is deeply flawed and probably biased to go against ML solutions for preconceived reasons.

sdoering 6y ago
> probably biased to go against ML solutions for preconceived reasons. Wow. Nice ad hominem. Thanks a lot for that. > Just reduction of operating costs alone is usually a reason to favor ML solutions I have yet to see one solution in the industries I work in and the clients I work with, were a ML solution beats simpler systems in development and operation costs (given the current real world environemnt there). And believe me I try to sell these projects to clients, as I strongly believe that in the long run they could gain something from that. But that would also mean getting rid of a clusterfuck of different systems, different data definitions from department a to department b as well as market x to market y. Politically motivated data mangling (we do not want "central" to know everything so we do not send all data or data in the necessary format). When you see that markets use technically the same CRM system for example, but they rename tables, drop columns, use same dimension names for different things and so on integrating one market into a central data lake becomes a daunting task, let alone 130 markets. And this is just CRM. Not sales. Not - given automotive - the data from retailer systems. But this would nonetheless be the data you need for ML systems to learn from. And then there are legal issues. car dealerships are separate legal entities. They are not allowed to "just" send PII date to the central brand (at least not with European GDPR). There is also a lot of stuff central just isn't legally allowed to know like discounts given - just to name one example. After you get all of this entagneld and cleaned up (and changing all necessary business processes that depend on said structures I strongly believe ML would probably be cheaper. And leading to better results. Don't think that I am telling my clients otherwise.

sdoering 6y ago

> probably biased to go against ML solutions for preconceived reasons. Wow. Nice ad hominem. Thanks a lot for that. > Just reduction of operating costs alone is usually a reason to favor ML solutions I have yet to see one solution in the industries I work in and the clients I work with, were a ML solution beats simpler systems in development and operation costs (given the current real world environemnt there). And believe me I try to sell these projects to clients, as I strongly believe that in the long run they could gain something from that. But that would also mean getting rid of a clusterfuck of different systems, different data definitions from department a to department b as well as market x to market y. Politically motivated data mangling (we do not want "central" to know everything so we do not send all data or data in the necessary format). When you see that markets use technically the same CRM system for example, but they rename tables, drop columns, use same dimension names for different things and so on integrating one market into a central data lake becomes a daunting task, let alone 130 markets. And this is just CRM. Not sales. Not - given automotive - the data from retailer systems. But this would nonetheless be the data you need for ML systems to learn from. And then there are legal issues. car dealerships are separate legal entities. They are not allowed to "just" send PII date to the central brand (at least not with European GDPR). There is also a lot of stuff central just isn't legally allowed to know like discounts given - just to name one example. After you get all of this entagneld and cleaned up (and changing all necessary business processes that depend on said structures I strongly believe ML would probably be cheaper. And leading to better results. Don't think that I am telling my clients otherwise.