31–40 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 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…
Hi @minimaxir, I just checked the readme of your project. It looks great, there's a mention to integrate Polyaxon[0] for running jobs in distributed way on Kubernetes. Let me know if you need any help with that. [1]: https://github.com/polyaxon/polyaxon/
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
[1] https://cloud.google.com/terms/service-terms#12-google-cloud...
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?
I do not think that you can compete with google on alg accuracy, since most of the underlying ML alg are open source (scikit learn or tensor flow). This is not a secret sauce.
That said, to get better models, you will probably need to find better hyper parameters tuning method, which depends on the number of models that you are willing to run per model tuning session. So if you have a unique model search method, you can save 10X-100X search time, which translate to real saving.
In addition, the solution lacks in model management. In general, most business people would like to understand why a specific model make a specific prediction. Most ops people want to track the training data version, model version, alg version etc.
Moreover, The product itself has "best practice" page :
https://cloud.google.com/automl-tables/docs/data-best-practi...
which include a list of features that are not in the product.
And one of the biggest differentiation should be on-prem vs cloud. Are customers willing to put their data in google (or any other cloud, for that matter) ? Can they legally do that?
I think that this product actually benefit the ecosystem since it helps to create a category of auto ml for tabular data, backed by google marketing budget.
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 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…