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An End-to-End AutoML Solution for Tabular Data at KaggleDays

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Re: An End-to-End AutoML Solution for Tabular Data at KaggleDays

#31

Post by kelvin0 »

bouk wrote: post #30
kelvin0 wrote: post #29

Let's just hope this does not become the 'Excel' of the ML space. Then anyone will start 'coding' some godawful models and use them in critical day to day infrastructure ... Don't get me wrong, I'm all for democratizing ML, but sometime these tools become fully-automatic-high-caliber footguns.

Excel is an amazing tool, and if this becomes the Excel of the ML space then it will be a great success

The horrors I've seen made in Excel still give me nightmares to this day. It's low barrier to entry is both a blessing and it's curse. But at any level this can be said of any tool under the sun ...

Re: An End-to-End AutoML Solution for Tabular Data at KaggleDays

#32

Post by cwilkes »

kelvin0 wrote: post #31
bouk wrote: post #30

Earlier quoted context omitted.

Excel is an amazing tool, and if this becomes the Excel of the ML space then it will be a great success

The horrors I've seen made in Excel still give me nightmares to this day. It's low barrier to entry is both a blessing and it's curse. But at any level this can be said of any tool under the sun ...

One man’s horror is another man’s only way to get anything done.

I used to be violently anti spreadsheet but have come around to being amazed what people can do with a very limited subset of tools. So instead of looking down upon them (not saying that you are) I admire what non technical people that just want to automate something can do.

Also I think spreadsheets are better than half of the code I’ve ever written as it is easier to extend without having to delve into the guts of some impossible to understand language.

Re: An End-to-End AutoML Solution for Tabular Data at KaggleDays

#33

Post by kmax12 »

I think it’s a bit of an overstatement to call this an end-to-end solution.

What they are starting with here is a single table of data with all the features already defined and an existing binary label column. Typically when this type of data is collected in the field it is much more fine grained (i.e many observations collected over time) and unlabeled (e.g how do we define a true example? How many false examples do we select?).

The competition description even goes as far to say “We have chosen a dataset that you can get started with easily”.

So, yes, this is a cool demonstration of Google's product, but the success in the competition might not extend to the problems real business face when trying to apply ML to a problem like this.

That being said, I do think AutoML can help with these problems as it is extended to handle data that isn’t in a single table already.

For example, I’m a developer of a open source library called Featuretools (https://github.com/Featuretools/featuretools) that tries to automate feature engineering for temporal and relational datasets. Basically, it helps data scientists prepare real world data into the form this competition starts with.

Re: An End-to-End AutoML Solution for Tabular Data at KaggleDays

#34

Post by mark_l_watson »

pplonski86 wrote: post #28
ptah wrote: post #9

Earlier quoted context omitted.

yes, and i bet they didn't use 2500 CPUs either

The first thing is, the Neural Networks are not golden ML algorithm that is working always the best. I'm not surprised to see GBDT methods working better than NN. The NNs are very powerful because they can accept the wide-range of data types (tabular, images, voice) which is not possible with GBDT (maybe not yet). The second, I think architecture search fo NNs is very inefficient right now. Most of the methods are tr…

You might like AdaNet that doe’s architecture search in one TensorFlow session and seems efficient (at least my use of it).

Re: An End-to-End AutoML Solution for Tabular Data at KaggleDays

#35

Post by fnbr »

yazr wrote: post #20
fnbr wrote: post #3

Earlier quoted context omitted.

(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…

DOes this 2500-cpu-hr cover the ENTIRE learning process? 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.

I'm not sure about the exact details, but that's my understanding too. It is very exciting. This is directly replacing a week of work (if not more!) of a qualified ML engineer. Additionally, as all of the experiments are cheap (being run on a single CPU) you can run them on the cheap, interruptible, cloud instances, and it's not the end of the world if you need to restart some experiments.

This is also not mature science- there's still a lot of active research being done on AutoML- so there's still a lot of potential for improvement.

Re: An End-to-End AutoML Solution for Tabular Data at KaggleDays

#36

Post by fnbr »

so_tired wrote: post #21
fnbr wrote: post #3

Earlier quoted context omitted.

(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…

Is there any way to "scale up" the solution network for more dimensions? For example, if i train on a a 100x100 image domain, with good results. So now I get a bigger budget to work with 200x200 images. There is no real way to leverage the good architecture from the 1st network. Is there ? Can this be done as a ugly-hack and then be used as a seed into the architecture-search ?

Not that I'm aware of. I could imagine a few things that you could try that might accomplish this, but I'm not aware of any published literature discussing the efficacy of such work.

You could do the naive thing, which would be to take your architecture and scale each layer size up (e.g. select an architecture on Cifar-10 and then scale it up to work on ImageNet). This is done in practice quite often, and seems to work well, but I'm not aware of any robust research done to validate the effectiveness of this.

Re: An End-to-End AutoML Solution for Tabular Data at KaggleDays

#37

Post by shereadsthenews »

rahimnathwani wrote: post #2

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 hope this isn't the real reason, but every engineer at Google has access to free and practically limitless CPU time quota, while GPU quota requires dedicated machines and therefore budget. It might have just been easier to do it on CPUs.

Still 2500 CPU-hours is basically nothing. That's how much time a single machine with 2 sockets and 112 threads yields in a single day.

Re: An End-to-End AutoML Solution for Tabular Data at KaggleDays

#38

Post by eyeball »

tixocloud wrote: post #23
filleokus wrote: post #15

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.

Auto ML is great as long as there is some rational on why a particular model is developed. However, in real world applications, there are so many constraints and considerations needed besides just having the most accurate model. Furthermore, as a VP of data science, selling the business on the value and benefits of data science is non-trivial as many are not even aware of what's capable with data science so my person…

>The only way it could work is to have an experienced data scientist supervise.

That appears to be the business model of https://www.datarobot.com/

automated tools with consulting data scientists to help clients use them effectively

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