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Amazon SageMaker Autopilot
- scribu 7y agoWould 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 7y agoThis might be intresting: https://medium.com/analytics-vidhya/google-automl-tables-a-first-taste-f25b3728e1a7 https://medium.com/analytics-vidhya/google-automl-tables-a-f...
- aantix 7y agoHow does the algorithm analyze the results and look for overfitting?
- scribu 7y agoProbably the same way a human would do it: By splitting the dataset into training, testing and validation buckets.
- zitterbewegung 7y agoProbably 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 .
- massaman_yams 7y agoBuilt-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.
- AlexCoventry 7y agoThis 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 7y agoWhat'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 7y agoRight, AutoML != AGI.
- dopylitty 7y agoIn 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.
- poxrud 7y agoThis 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...
- AlexCoventry 7y agoDo any of those services reliably give useful results?
- voiper1 7y agoWait, 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 7y agoYou say that like that's the easy part of ML.
- throwawaytemp1 7y agoit's what upper management rewards if the senior scientists who can't even fizzbuzz at Amazon are any indication
- pc86 7y agoI 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.
- nonfamous 7y agoAzure Machine Learning has an Automated ML service that does this: https://docs.microsoft.com/en-us/azure/machine-learning/service/concept-automated-ml https://docs.microsoft.com/en-us/azure/machine-learning/serv...
- normalocity 7y agoPay-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.
- sixdimensional 7y agoI 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.
- ralusek 7y agoI 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 7y agoNo. 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).
- eyeball 7y agoI wonder how this compares features and price to similar products from H2O.ai’s driverless ai, datarobot, bigsquid, etc.
- amrrs 7y agoH2O'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 7y agoAny 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.
- turingbike 7y agoGoogle'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...
- newusername2 7y agoGCP 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-pipeline https://cloud.google.com/ml-engine/docs/scikit/custom-pipeli... https://cloud.google.com/ml-engine/docs/algorithms/xgboost-start https://cloud.google.com/ml-engine/docs/algorithms/xgboost-s... https://cloud.google.com/ml-engine/docs/tensorflow/getting-started-keras 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-container https://cloud.google.com/ai-platform/notebooks/docs/custom-c... Disclaimer: I work for Google.
- streetcat1 7y agoRight, but your automl solution is using Tensorflow, no? (even for automl-tables).
- elithrar 7y agoI’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#export_to_devices https://cloud.google.com/vision/automl/docs/export-edge#expo... [1]: https://www.sparkfun.com/categories/tags/tensorflow https://www.sparkfun.com/categories/tags/tensorflow
- ckvamme 7y agoYou 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
- amrrs 7y agoIn 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-from-kaggle-survey https://www.kaggle.com/nulldata/carving-out-the-automl-niche...
- m23khan 7y agoThis 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 7y agoMy 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.
- m23khan 7y agogood point!
- streetcat1 7y agoRight. 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).
- deleted 7y ago[deleted]
- gbrits 7y agoDo 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.
- streetcat1 7y agoYou can use web-grpc to generate js api from grpc interface. There is also tensorflow.js .
- samcodes 7y agoSince 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.
- sandeepngupta 7y agoIf 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-tutorial https://cloud.google.com/vision/automl/docs/tensorflow-js-tu...
- aledalgrande 7y agoPretty 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.