Amazon SageMaker Autopilot (opens in new tab)

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... [1]: https://www.sparkfun.com/categories/tags/tensorflow

GCP supports training scikit / xgboost/ Tensorflow models and exporting them to Cloud Storage for use elsewhere.

More here: https://cloud.google.com/ml-engine/docs/scikit/custom-pipeli... https://cloud.google.com/ml-engine/docs/algorithms/xgboost-s... https://cloud.google.com/ml-engine/docs/tensorflow/getting-s...

Creating custom notebook containers is now also supported with AI Platform: https://cloud.google.com/ai-platform/notebooks/docs/custom-c...

Disclaimer: I work for Google.

I agree, it was good to see the option to generate Python code that can be reused, tweaked, etc.

Can we stop with the Google FUD re shutdowns? Browsing hacker news to any post about Google at any time in the past year you'll only ever see discussions on how "google shuts things down".

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.

Azure Machine Learning has an Automated ML service that does this: https://docs.microsoft.com/en-us/azure/machine-learning/serv...

Pay-as-you-go compute pricing means this is $$$, which I'm guessing is the tradeoff. But hey, classic time/money tradeoff for some level of automated (non-customized) performance -- should be good for a bunch of common use cases that would otherwise require hiring a human, like many generic technologies.

I am not 100% sure but I think that is also exactly what datarobot.com does as well. I don't work for them, and haven't used their tools, so I have no idea how good they are, have just seem some demos before.

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

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.

In case your comment is about the frequency of announcements rather than the subject I just wanted to mention AWS is having their main yearly conference this week so they will be announcing many new services and features.

This is not new technology. Th is is AWS catching up to the competitors. There's Google AutoML, AutoKeras, Azure Automated ML, IBM AutoAI, H20, and others...

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...

Since this produces a notebook with python code, you might be able to tweak it so the final model works in tensor flow.js. But depending on model size / hardware requirements, it might be better to just make a network call.

You can use web-grpc to generate js api from grpc interface. There is also tensorflow.js .

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.

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.

Probably the same way a human would do it: By splitting the dataset into training, testing and validation buckets.

Probably some rule based expert system to analyze results . Looking for overfitting could be done by creating validity set. So you would have test, train, and validity .

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

This might be intresting:

https://medium.com/analytics-vidhya/google-automl-tables-a-f...

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).