Only a fool would use any of these platforms and large companies never will. The ml industry is full of fledgling products that no one should ever use and clearly if you use this you will be locked into google forever
Google launches an end-to-end AI platform
51–60 of 132 posts
Re: Google launches an end-to-end AI platform
#52Make 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…
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.
Re: Google launches an end-to-end AI platform
#53I've tried using Google's AutoML to classify medical images as part of a Kaggle competition just to see what their process is like and how well it performs. This was Q4 2018, so things might have changed slightly.
In brief, my experience was quite frustrating. First, getting my dataset to the cloud required quite a bit of manual labor. Uploading my 25GB of images on my 40mbit/s wasn't really ideal so I ended up spawning an virtual machine on GCP and downloading directly from Kaggle. Unzipping the files and writing it to Google Cloud storage with some terminal commands.
Furthermore, to get the label data into AutoML I had to write some generation script that generated the exact format CSV that AutoML requires - which was hidden in their documentation somewhere with no mention in the AutoML environment itself.
Nothing too cumbersome but generally not a very user friendly experience, or something I wish to repeat many times if I get a new/different dataset.
Ultimately, when the data with labels was in the model started training. Then I found, that I didn't really have the tools and information to assess the model performance. They did a decent job of characterizing model performance through precision recall graphs and displaying incorrect predictions but that didn't really satisfy me. I was interested in getting more details about where it was misclassifying images, specifically how classification performance was distributed across the 28 classes the model was predicting (in a multi label context).
This is the point where I think the downside of working with a platform such as AutoML starts showing. I tried reaching out to someone about gaining more insight into model performance by opening a ticket, since there was no phone number. After a couple of days I finally received an email from a product representative that told me that for any assistance I should contact one of their local cloud partners.
These are third party vendors that typically assist companies in deploying cloud based applications in the GCP. However, after calling two of these companies that were highly recommended by Google's vendor page I was told that they don't have any experience with AutoML and that I was on my own. The other company didn't reply at all.
In my view, choosing a product such as AutoML - for a company that is serious about adopting AI to improve their business - is currently not a good path (yet). And I see this space as being wide open for competition with current solutions not cutting it from my point of view.
Re: Google launches an end-to-end AI platform
#54This 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?
You might consider a strategy where you offer a drop-in compatible service for people not reading Google's terms and not understanding the limitations. Let Google do the marketing for you, people will come to you, when Google kicks them out of their smart TV
Re: Google launches an end-to-end AI platform
#55I cant find in the documentation what kind of algorithms they are using? Is it only one algorithm type (neural network)?
Re: Google launches an end-to-end AI platform
#56Paraphrased from the article: AutoML tables takes a generic table and predicts can predict a columns value (for an unseen partial row, I assume). Their product page emphasises how easy it is: for developers with limited machine learning experience. What are the consequences of using such structured data predictions devoid of interpretation or quantified uncertainty? Presumably such predictions could be from a set of…
AutoML is essentially training a ML model using some heuristics or optimization algorithm to select model architecture and train a model. Feature engineering / feature synthesis as well as interpretability remain open challenges. If I'm understanding your questions correctly, the main problems I see with this are: - Using raw data instead of feature engineering (less of a problem given feature synthesis libraries lik…
Another common error I see is timing (e.g. using data from the "future" to predict an event). To build on your loan example, if you inadvertently included the current FICO score of an applicant that applied 12 months ago, it will be unfairly correlated with the loans current performance.
Re: Google launches an end-to-end AI platform
#57Neat. 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.
Re: Google launches an end-to-end AI platform
#58The point of autoML from what I've gathered is to make it as easy as can be to get a model working in production. AutoML at least AutoML Vision is using transfer learning to retrain X number of layers from their algorithm (the one google uses is escaping me right now). The number of layers it has to retrain is the value they offer, it tries and tries optimizing for accuracy.
I've had good results with it, but you do have to do things their way and its not always well documented. If you want more control you should create your own model and host it on google app engine, otherwise AutoML is what it is, no way to customize or tune it other than changing the training data you give it.
Re: Google launches an end-to-end AI platform
#59It’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.”
Cloud vendor lock-in and proprietary hardware, software, _and_ datasets is not in any way "democratizing" anything.
>> not really because they have useful work for these folks to do
Re: Google launches an end-to-end AI platform
#60Earlier 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.
No, because using it to get the prototype going is "using these Services to create (directly or indirectly)". Frankly, those terms look poisonous - you could be liable if any part of your service has origins in Google's platform. If you infringe on the GPL, you can remove the offending code. Here, you have no way to disentangle yourself from Google's trap.