AWS consistently releases similar products after GCP... but they are much more well-thought-out, as AWS has to support them indefinitely...
Creating custom notebook containers is now also supported with AI Platform:
Disclaimer: I work for Google.
I get the trope, you want upvotes, it's an easy joke, and you can say it without real affliction at this point, but can we instead discuss the technical merit at hand — in this case AWS?
In this specific case, as noted by several other comments, you can also export GCP models.
Any other places do this?
I read "just" as surprise it's this easy from the end user perspective, as opposed to the actual technology.
Like is this just adding standard layers to a neural net, maybe trying a few activation functions, fiddling with the number of layers and just seeing which give the best results?
If I read it correctly this is using traditional "classical" ML models (e.g. XGBoost, GBM and even linear models).
First of all, which models are going to be used? How many combinations of hyperparameters are going to be tried? The combinatorial explosion is certain.
And then if you don't know how to prepare the right dataset everything is in vain.
Not really a critique to AWS, but to AutoML in general.
EDIT: After a deeper read it seems it's regressions on textual data only.
It looks pretty slick. Going to install the free version to check it out.
Does this mean that going forward, for small-to-mid size IT companies and Corporates, the demand for Data scientists and ML developers would decrease?
ML is finicky; the model training pipeline itself isn’t the hard part, compared to setting up for the right question and examples used to train the model.
For small-to-mid firms, data scientists are super expensive. And they might only deliver a valuable project every six weeks (or, at bigger firms, every year...).
If automl increases their productivity, suddenly they don’t look so expensive.
However, the job of the DS will move toward the business side (e.g. req gathering, data gathering and prep) and less about the modeling itself.
Also, there are a lot of data issues that are still in the releam of humans (e.g. imbalance data, correct labeling, etc).
That is definitely a technically challenging problem but not an impossible one.
If anything AWS is late to this.