I dunno, there's clearly a bar for "good enough". From the article it's not obvious to me that AWS Rekognition meets that bar (yet). 21% precision with 54% recall isn't the kind of thing that achieves "if you're using AI and your competitors aren't, you're ahead".
I like your framing/sentiment though: it's not about small differences in "betterness", it's about the difference in kind in going from "no ML/AI" => "whoa, it works!".
Disclosure: I work on Google Cloud (but not in ML).
> Disclosure: I work on Google Cloud (but not in ML).
Since you work there, hopefully you can see this feedback and filter it up: I love your tools. They are the best. I have tried using your tools. They are hard to use, despite the fact that I have a pretty solid understanding of how to use them. I would like to use your tools more, but getting support is hard (partly because the docs aren't great, partly because there is no community, because see #1).
I don't know how to fix this, but it would be great if maybe Google spent some time focusing on building a community around your tools, like AWS did. At the beginning they had a lot of employees hanging out on the forums and on other forums, answer questions and building a community of users, and especially helping third parties who tried to build libraries for their tools (like boto for Python). It would be great if Google did that too.
Google has contributed quite a bit to Boto. The GCS command line tool, gsutil, is built on top of boto. If you're talking about Boto 3, though, not so much.
I like your framing/sentiment though: it's not about small differences in "betterness", it's about the difference in kind in going from "no ML/AI" => "whoa, it works!".
Disclosure: I work on Google Cloud (but not in ML).