> backed by a team surely comprised of at least hundreds of top Amazon engineers, was so slow and inaccurate.
You'd be surprised. Many many AWS services are launched by a single 2-pizza team (8-10 developers). It's in Amazon's DNA to act like a startup, ship something quickly, collect feedback, then iterate, and scale.
Also, from what I’ve been able to deduce, the AWS services are all just built on top of EC2 [un]modified open-source software until they reach a level of complexity where they roll their own, which leads to lots of issues that can take years to fix. Some things are telling, like banning two consecutive hyphens because something somewhere in that pipeline is shelling out.
Also, for the record, AWS is hardly top of the industry in terms of talent. It's not Google or Facebook. I've never heard of an ML researcher joining Amazon when they could have joined Google/FB/Apple.
I've known plenty of people that have left Google/FB/Apple for Amazon. And I know plenty of people who have left Amazon for Google/FB/Apple. The biggest tech companies are quite incestuous. Some people do thrive in one environment, some in others. They all pay top dollar.
It could happen both way. Noted that Google is much more saturate for its ML talents, so unless you are super top-notch, some people will take it to Amazon for bigger impact. I personally know a few people, who is competitive in the industry, having Google offer/leaving Google for Amazon.
No doubt, the flow is usually Amazon -> Google/Facebook, because for mid-level engineer/scientist, money talks :), and Google/FB generally have much open/better publication policy, while Amazon is neurotically secretive. Apple is not on the picture, I think the internal engineer culture is very problematic(no common codebase, severe NIH syndrome), except for the money, it is subpar place to grow.
You'd be surprised. Many many AWS services are launched by a single 2-pizza team (8-10 developers). It's in Amazon's DNA to act like a startup, ship something quickly, collect feedback, then iterate, and scale.
Rekognition will get much better in time.