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[D] Is there such a thing as a generic ML Solutions Architect? Does the concept even make sense at all?

I work in a highly specific field, where ML and Stats knowledge is important, but domain and business knowledge are also absolutely crucial to being able to do anything of substance.

A friend of mine who works in the same field, and is really good at what he does, was recently contacted by a major tech company to be an ML Solutions architect, based on his experience with some of the latest cloud platforms. The role they are hiring him for is “general” ML Solutions Architect, not tied to any specific domain: In theory he should be able to go to anyone of their clients, whether a bank, or a manufacturer, or a mobile gaming company, or a retailer, etc…and be able to advise them on how to build ML solutions in the cloud.

I’m kind of surprised by this: You might be able to port ML development skills or data engineering skills from one domain to another, but Solution Architecture is a completely different beast, and I don’t see how somebody, know matter how smart and adaptable they are, can make informed decisions about an enterprise ML architecture without deep experience in that particular domain.

What are the skills that are specific to ML Solution Architecture (as opposed to being just general ML or Data Engineering knowledge) yet are same time are not domain specific and applicable to general enterprise contexts?!?!

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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.