81–90 of 132 posts
This will make a bunch of startup's life really hard. I think it makes it harder to justify investing in your own ML pipeline or even building your own models for many use cases.
This will make a bunch of startups' lives really hard... when Google gets bored with it and kills it just when a lot of startups have come to depend on it.
Earlier quoted context omitted.
I agree with you that the supply of those workers is low (I am one of them! And hiring more is extremely hard!) ... but it doesn’t mean Google / FB / etc have meaningful work for them. A friend of mine was hired as a senior ML person at Facebook and ended up working solely on cartoon avatars and page responsiveness / latency optimization (not using ML). When he raised the issue to managers that he wasn’t working on a…
As an ex-Googler, that's not how you're supposed to operate at those companies. You find something you like to do, talk to the team, ensure the other team has open spots and would like to take you on, and move your shit from one desk to the other. Done. You're working on your specialization, if that's what you like to do. Discussing stuff with your manager is utterly pointless because your interests aren't really ali…
Just move your shit to another desk and join whatever team you want with out talking to your manager or hr or updating your goals?
Yeah... nah.
This will make a bunch of startup's life really hard. I think it makes it harder to justify investing in your own ML pipeline or even building your own models for many use cases.
Most people including ones here don't understand the value prop. In ML, the model (and modeling) is an afterthought that takes 3 lines of code. The value-add isn't the model or modeling, it's the feature generation and methodology. When (and if) this gets automated then yes, it will make life hard on many data scientists. But I don't see it happening within 20 years and most likely not within my lifetime.
Earlier quoted context omitted.
Please don't post glib cliched comments. There's a valid point behind this but the same one-liner appearing several times on every Google product announcement is just tiresome.
You say 'glib', but I actually want the answer to the question, so I know whether to bother investing any time learning about this or not.
No, you don't. Because any rational person would know that nobody is going to have or tell you the answer. So, still glib and disingenuous to boot.
Earlier quoted context omitted.
As an ex-Googler, that's not how you're supposed to operate at those companies. You find something you like to do, talk to the team, ensure the other team has open spots and would like to take you on, and move your shit from one desk to the other. Done. You're working on your specialization, if that's what you like to do. Discussing stuff with your manager is utterly pointless because your interests aren't really ali…
That might be how Valve works, but I’m quite sure it’s not how Apple / Facebook / Google do. Just move your shit to another desk and join whatever team you want with out talking to your manager or hr or updating your goals? Yeah... nah.
Don't know about Apple or Amazon but Google/FB are like that. If you're very senior, a few months (no more than 6 no matter how senior) delay might be imposed so that you hand off your stuff, if you're less senior, a few weeks is usually enough. It is also expected (at Google, don't know about FB) that you'll stay on each team for at least a year, and that you'll wind down your obligations in an orderly fashion, which I think you'll agree is not unreasonable.
But _nobody_ will force you to do work you really don't like to do. In contrast, at most other companies it's easier to get a job _at another company_ than to move to another team.
Make sure you've read the service terms[1] if you plan on building apps for speakers, cars, TVs or smart watches... 12.1 The following terms apply only to current and future Google Cloud Platform Machine Learning Services specifically listed in the "Google Cloud Platform Machine Learning Services Group" category on the Google Cloud Platform Services Summary page: Customer will not, and will not allow third parties to…
This will make a bunch of startup's life really hard. I think it makes it harder to justify investing in your own ML pipeline or even building your own models for many use cases.
I'm running a startup which offers the same solution as Google AuotML Tables. Recently I decided to go open source. I will need to compare my solution with Google AutoML Tables (compare in terms of final model accuracy). But anyway I think many times the best model accuracy is not the most important in ML solutions. Any ideas what can I do with such a situation with my solution? Can I compete with Google?
Below is an excerpt their T&C, use those to your advantage. I can't imagine any serious enterprise customer wanting to tie them down to Google. Just tell the market that you do exactly the same as this platform without tieing them down, without privacy violation and without letting them down by abandoning the product (yours is open source)
God speed!
12.1 The following terms apply only to current and future Google Cloud Platform Machine Learning Services specifically listed in the "Google Cloud Platform Machine Learning Services Group" category on the Google Cloud Platform Services Summary page:
Customer will not, and will not allow third parties to: (i) use these Services to create, train, or improve (directly or indirectly) a similar or competing product or service or (ii) integrate these Services with any applications for any embedded devices such as cars, TVs, appliances, or speakers without Google's prior written permission. These Services can only be integrated with applications for the following personal computing devices: smartphones, tablets, laptops, and desktops
This is being announced now in the Google Next keynote. This platform focuses not on the this-AI-is-magic-and-can-solve-everything like many AI SaaS startups announced on Hacker News, but focuses on how to actually integrate this AI into production workflows , which is something I wish was discussed more often in AI. The announcements here, including AutoML Tables (which is coincidentally similar to my own Python pac…
This is where Google really has a big lead on AWS -- the AI space. AWS has AI tools, but Google's are better and easier to use. The big question is: If all your data sits in AWS, because your app that generates the data is there, do you reach across and try to use the Google AI tools, or are their tools compelling enough to get you to move your app and all your data to GCS?