
Amazon SageMaker Autopilot - jpetrucc
https://aws.amazon.com/blogs/aws/amazon-sagemaker-autopilot-fully-managed-automatic-machine-learning/
======
turingbike
Google's AutoML produces black box models that are only available over a
network call. This services seems to produces downloadable models, and a
notebook with Python code that creates the model. If that is the case, this is
substantially better than GCP's offering.

AWS consistently releases similar products after GCP... but they are much more
well-thought-out, as AWS has to support them indefinitely...

~~~
elithrar
I’m not sure why the other post was marked dead, but AutoML models can be
exported - you can export them to TFLite format[0] and then run them on edge
devices, such as a SparkFun Edge[1] or a Coral SoC / board.

[0]: [https://cloud.google.com/vision/automl/docs/export-
edge#expo...](https://cloud.google.com/vision/automl/docs/export-
edge#export_to_devices) [1]:
[https://www.sparkfun.com/categories/tags/tensorflow](https://www.sparkfun.com/categories/tags/tensorflow)

~~~
ckvamme
You can also export your model as a server within a Docker container:

[https://cloud.google.com/automl-tables/docs/model-
export](https://cloud.google.com/automl-tables/docs/model-export)

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scribu
Would be useful to have a comparison to Google's AutoML Tables:
[https://cloud.google.com/automl-
tables/docs/features](https://cloud.google.com/automl-tables/docs/features)

~~~
streetcat1
This might be intresting:

[https://medium.com/analytics-vidhya/google-automl-tables-
a-f...](https://medium.com/analytics-vidhya/google-automl-tables-a-first-
taste-f25b3728e1a7)

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aledalgrande
Pretty neat, but unfortunately I cannot see a lot of business cases for this.
I haven't worked with a ton of models, but especially if you are not dealing
with pretty much solved problems like classification, the results won't be
great.

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.

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voiper1
Wait, really, I just upload tables of input and the expected output data and
it tries various models for me?

Any other places do this?

~~~
jgalt212
You say that like that's the easy part of ML.

~~~
pc86
I think you're misunderstanding the comment.

I read "just" as surprise it's this easy from the end user perspective, as
opposed to the actual technology.

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ralusek
I assume this won't do things like add convolutional layers if you give it
pixel or signal data, right?

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?

~~~
streetcat1
No. auto ml for DNN is a different ball game (Also known as Neural
architecture search).

If I read it correctly this is using traditional "classical" ML models (e.g.
XGBoost, GBM and even linear models).

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amrrs
In general, If you're interested in looking into AutoML landscape and its
adoption here's a Kaggle kernel based on recent Kaggle Survey
[https://www.kaggle.com/nulldata/carving-out-the-automl-
niche...](https://www.kaggle.com/nulldata/carving-out-the-automl-niche-from-
kaggle-survey)

------
gbrits
Do any of these autoML offerings have a way to use the generated model in
JavaScript/nodejs? I know of [sklearn-porter]([https://github.com/nok/sklearn-
porter](https://github.com/nok/sklearn-porter)) which transpiles scikit-learn
models to JavaScript among other targets, but not sure if this nicely connects
with any of the solutions discussed.

~~~
sandeepngupta
If you use GCP AutoML service, you can export AutoML Vision edge models (for
image classification and object detection) directly for TensorFlow.js for use
in browser or Node.js. Please see this:
[https://cloud.google.com/vision/automl/docs/tensorflow-js-
tu...](https://cloud.google.com/vision/automl/docs/tensorflow-js-tutorial)

------
eyeball
I wonder how this compares features and price to similar products from
H2O.ai’s driverless ai, datarobot, bigsquid, etc.

~~~
amrrs
H2O's main pitch is that except Driverless they're open source. Data robot
seems to have got a strong Salesforce for each domain and driving sales. In
terms of features, Google cloud AutoMl seems better as it makes the entire
productionising part easy

~~~
eyeball
Any experience with the datarobot tool? The few demos/YouTube’s I’ve seen, it
looks really slick. Lots of pre-built performance evaluation, model
deployment/monitoring tools, etc. hard to find any pricing info on the web.

~~~
owlninja
We recently purchased dataiku and I've found it to be really quite handy.

~~~
eyeball
Any info on pricing? I can’t find anything on their website. Don’t like taking
to sales goons.

It looks pretty slick. Going to install the free version to check it out.

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m23khan
This is rather interesting development. Just last week I saw similar feature
in IBM Watson being demoed on IBM Cloud. And now AWS Sagemaker has this
capability.

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?

~~~
jpau
My guess is it will, on average, increase demand.

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.

~~~
streetcat1
Right. It will increase demand, since many area of the business will start
using machine learning.

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).

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aantix
How does the algorithm analyze the results and look for overfitting?

~~~
massaman_yams
Built-in regularization, probably, plus cross-validation. These techniques
aren't new; they're included in a number of ML libraries already - just not at
this level of automation.

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AlexCoventry
This is the second science-fiction-level announcement from Amazon in as many
days. Either they're about to take over the world with effective AGI and
Quantum Computation, or they're being a bit silly.

~~~
whoisjuan
What's science fiction about doing auto-machine learning? It's basically
automation of a lot of the tasks that data scientists do manually to build
simple ML models.

That is definitely a technically challenging problem but not an impossible
one.

If anything AWS is late to this.

~~~
massaman_yams
Right, AutoML != AGI.

