Any ideas what can I do with such a situation with my solution? Can I compete with Google?
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.
Depends on how much you're paying them and the SOW you've signed.
> non transparant pricings and they disrespect privacy.
For pricing, talk with them. Or use a cloud broker.
You’re joking, right? Every other day there’s a top 10 article on HN about Google locking out a whole business customer with no humans to speak with.
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
[1] If the tool is your primary value add, I think you're making a mistake in open sourcing it.
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.
Generally, if a model is not performing or generating unexpected results, its almost always the data or how the question is being structured.
We’re also a startup in the ML space but solely focused on production deployment.
You’re right in that model accuracy is not the most important thing. There are many other considerations as to why a model should be used including training time, processing costs, value.
Furthermore, as someone mentioned earlier, Google’s service is horrendous and provides another angle to address. Their reputation as a company to be trusted with data is also somewhat shaky given all the privacy concerns.
Add on top of that better customer support, like other replies suggested, and your product won't be dying due to competition from Google any time soon.