I am too building a new automl platform on top of kubernetes, I do not think that you can compete with google on alg accuracy, since most of the underlying ML

I am too building a new automl platform on top of kubernetes, 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-practices 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.