11–20 of 38 posts
> Erkut Aykutlug and Mark Peng used XGBoost with creative feature engineering whereas AutoML uses both neural network and gradient boosting tree (TFBT) with automatic feature engineering and hyperparameter tuning. It's hilarious that gradient boosted descent tree beat Google's fancy AutoML-generated neural networks.
yes, and i bet they didn't use 2500 CPUs either
Can automl be replicated outside of google cloud?
I wonder how automatic machine learning tools like these will shape the "data science" roles in the future. Obviously, the most cutting edge research will always be done by specialised human experts, but perhaps tools like these will lower the bar required for the bulk of mainstream ML work.
How does these auto ML-solutions (like h2o) work in practice, anyone willing to share their experience? I wonder how automatic machine learning tools like these will shape the "data science" roles in the future. Obviously, the most cutting edge research will always be done by specialised human experts, but perhaps tools like these will lower the bar required for the bulk of mainstream ML work.
> Erkut Aykutlug and Mark Peng used XGBoost with creative feature engineering whereas AutoML uses both neural network and gradient boosting tree (TFBT) with automatic feature engineering and hyperparameter tuning. It's hilarious that gradient boosted descent tree beat Google's fancy AutoML-generated neural networks.
Does someone know what process they use for feature engineering?
>When you kick off training, AutoML Tables automatically performs common feature engineering tasks for you, including:
>Normalize and bucketize numeric features.
>Create one-hot encoding and embeddings for categorical features.
>Perform basic processing for text features.
>Extract date- and time-related features from Timestamp columns.
Source: https://cloud.google.com/automl-tables/docs/features#feature...
> Erkut Aykutlug and Mark Peng used XGBoost with creative feature engineering whereas AutoML uses both neural network and gradient boosting tree (TFBT) with automatic feature engineering and hyperparameter tuning. It's hilarious that gradient boosted descent tree beat Google's fancy AutoML-generated neural networks.
I'm interested to know how easy this for regular people (software engineers with just a little knowledge of data science) to use. This part stands out: "our team spent most of time monitoring jobs and and waiting for them to finish. Our solution for second place on the final leaderboard required 1 hour on 2500 CPUs" Before I got to this part, I had assumed using AutoML would involve only reformatting the training/val…
(I'm a researcher in ML/DL.) > Anyone know why they used CPUs instead of GPUs/TPUs? Cost and resource availability. They're distributing each computation on a different CPU, not distributing each computation over multiple CPUs. It would be faster to have each computation run on a CPU + GPU, but that would be very very expensive, and hard to schedule. GPUs/TPUs are also only faster for sufficiently large networks and…
Lets even your first run is crap, and you try again with RGB instead of YUV or whatever. So you do 4 runs.
So 10000-cpu-hours replace a week of work of a qualified ML engineer. This is pretty amazing. If i understand correctly.