61–70 of 132 posts
Neat. How long until they shut it down?
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.
Earlier quoted context omitted.
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?
Usually google is good in the initial release of a product, but they lack good customer care and support. They have non transparant pricings and they disrespect privacy. Those are something's you can differentiate on
I'm waiting right now for their model performance score. But I got feeling that they are only tuning Neural Networks. (however I cant find info about algorithms they are using).
Maybe this is crazy, but I feel that I can compete with them on model accuracy and UI. For sure, I cant compete with them on marketing.
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.
I am not sure what you mean by methodology?
The one thing that I, as a tool developer, do not understand is how humans would be able to handle hundreds of models, including retraining/monitoring and deployment without automl.
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…
Looks like Google is taking over Cloud (from AWS) for AI by building an ecosystem and building tools for non Data scientists - consumer level product. Surely IBM can do similar thing with their recent Redhat acquisition, but will they ?
Earlier quoted context omitted.
I have a hard time imagining who this wouldn't be a dealbreaker for. These terms also mean you can't use it for open source, and you can't use it if you don't know what the ultimate application is going to be. And you probably can't resell technology you create, because no one who buys it is going to want that restriction, either.
Whatever get your prototype going, get funding, build it on open source.
I work in building and deploying production ML/AI models but I'm having a lot of trouble cutting through the marketing jargon in this article and on Google's website as well. Can someone explain what this does in engineering terms? How does this differ from something like AWS Sagemaker?
With sagemaker you still need to provide the python code for training.
Neat. How long until they shut it down?
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.
Google is the one to blame for this being tiresome.
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.
It’s so disingenuous for Google to brand these efforts as “democratizing AI.” It is precisely the opposite. This is classic commoditization of your complement. On one hand, Google is pushing to centralize the integration, data management and computing platforms for machine learning, so that these things become as much of a commodity as possible. On the other side, they are offering massive compensation packages or ac…
>> It’s so disingenuous for Google to brand >> these efforts as “democratizing AI.” Exactly. NVIDIA building fast consumer GPUs and CUDA/cuDNN is "democratizing AI". FB (and Google) releasing open source deep learning toolkits is "democratizing AI". People releasing reproducible research code and datasets are "democratizing AI". Cloud vendor lock-in and proprietary hardware, software, _and_ datasets is not in any way…
When he raised the issue to managers that he wasn’t working on anything related to his specialization (deep learning for NLP) and this made him unhappy, the response was essentially, “Get in line.” I’ve heard similar stories about Google from a former boss who had been a long time manager in Google.
You essentially get paid super well to be put out to pasture so that your skill isn’t being used by other companies (leading to more demand for Google’s managed AI solutions).
In order to get career-developing work, you have to play political games or get hired in a non-standard way, like acqui-hire or poached, where you can negotiate your projects as part of your hiring conditions. Eventhen it will probably only be respected for a short time while it’s convenient for Google, and they’ll find a way to manage you out of that situation when they want to.
There are some tremendously talented AI engineers in places like Google. Some of them create awesome products and tools. A bunch of others sit around and atrophy working on dumb shit locked in golden handcuffs just to ensure they’re not on the market and able to help a company build things in-house more cheaply / more optimally than if they needed to buy it through some managed services through Google.
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?