The way in which Google released TensorFlow (and the meteoric success it's enjoyed) is an excellent example of a company successfully commoditizing its complements using open source software.

TensorFlow (and stacks built over it) has the greatest mindshare, which allows Google to both optimize the capabilities of its ML offerings based on TensorFlow, knowing most developers will be familiar with it, and to increase profit margins on those services at the same time. TensorFlow makes a significant part of the ML stack convenient and accessible, and releasing it for free opened the flood gates for Google to expand the machine learning industry significantly while shifting the profit generation away from software and onto the cloud (data storage + compute resources).

Sublime execution, really. Once machine learning passes through this hype cycle (over the "trough of disillusionment" and onto the "plateau of productivity"), Google will likely have made the majority of the machine learning industry accessible enough for most software engineers to pick up something like TensorFlow and hit the ground running, which will gradually exert a downward pressure on ML specialized salaries for all but the top end. But this will continue even as the industry continues to expand, which means Google will come out ahead earning even more profit even if salaries plateau or decrease.

Two problems. 1) Tensorflow sucks 2) People that are actually experts in deep learning prefer Pytorch or Caffe2 so your mindshare thing is wrong, once the dust of the Tensorflow hype train clears people will choose tools that are actually any good

Why do you believe "Tensorflow sucks", and why do you believe PyTorch or Caffe2 is better, and for what metrics?

Two metrics stood out in my experiments with CNNs back in May 2017:

1) Compilation speed for a jumbo CNN architecture: Tensorflow took 13+ minutes to start training every time network architecture was modified, while PyTorch started training in just over 1 minute. PyT suits my style of interactive coding far better.

2) Memory footprint: I was able to fit 30% larger batch size for PyTorch over Tensorflow on Titan X cards. Exact same CNN architecture.

Both frameworks had major releases since May, so I am sure these metrics might have changed by now. However I ended up adopting PyT for my project.

This post is spot on.

Google is actually very scared of PyTorch since nearly all new AI research papers are using PyTorch. This means in 1-2 years or so, most companies will be using PyTorch for training and Caffe for deployment. Tensorflow doesn't even have an implementation of the latest ImageNet winner, DenseNet, but PyTorch does!

I have sources on the Tensorflow team saying they are scrambling to make another higher level wrapper for tensorflow at the level of PyTorch.

Jumbo CNNs are not the battleground. The real battleground is distribution. The first framework that scales out without placing much onus on the programmer will win, IMO. Facebook already showed that Caffe2 scales to 256 GPUs for imagenet. Tensorflow need to show it can scale as well. PyTorch needs to work on usability - model serving, integration in ecosystems like Hadoop, etc.

Today I learned: "commoditize your complements". Quite interesting compact way to put it.

It apparently isn't a new strategy, and is in the shared internet mind sphere. https://www.joelonsoftware.com/2002/06/12/strategy-letter-v/

As another commenter mentioned, I can't take credit for it. I first read it from Joel's blog.

The corporations that got data are also very interested in open development of machine learning algorithms. They know that the data is key for getting value out of the algorithms anyway...

EDIT: I second that regardless of motivation, this is a step forward.

You got it right. Tensorflow is HUGE in the AI community, Facebook and Microsoft are scared. I, for one, am a huge fan of Tensorflow and don't plan on learning anything new to do the work I'm already doing. Facebook and Microsoft should just join Google's coalition and use Tensorflow company-wide.

Actually, Tensorflow is huge in the data science community, but PyTorch is taking over ML research. TF is last year's cool thing. That's one reason why fast.ai just adopted Pytorch for their courses. Tensorflow is surprisingly low-level and needs libraries like Keras to be used easily. The field of Python ML tools moves fast...

I hope the ML community won't have to suffer the same kind of fatigue syndrom that has plagued the javascript community for the last decade.

Excuse the newbie question but are PyTorch and/or TensorFlow sufficient for developing ML algorithms that can be used directly by a user-facing application? Or is there typically some translation layer involved?

Right, until you don't like something that Google does with Tensorflow in the future or breaks compatibility with your app. I think alternatives in complex abstractions like these are always a good thing. Only a few companies can afford to do it so we have a guarantee that you won't have tens of them. Just a viable alternative.

It's been a while since I played around with tensorflow, how difficult is it to deploy a model as a web service? I'm currently using azure and looking for other options (besides prediction.io).

It's really designed around ease of use. What you're looking for is called TensorFlow Serving.

Cool, thanks for the info.

I'd love to know what sort of ML/AI communities/forums/slacks/discords/whatever communities you participate in, since I'm interesting in joining some.

I don't take Tensorflow seriously anymore... PyTorch is the way to go (gotta try caffe2 at some point).

The implication is that this will make your ML better somehow. Not sure why that would be.

from the article

> We developed ONNX together with Microsoft to bridge this gap and to empower AI developers to choose the framework that fits the current stage of their project and easily switch between frameworks as the project evolves.

ML workflows tend to be very heterogenous with people using everything from sklearn to DL4J to play with data. standardized serialization formats make it possible to bring results from those experiments back into a hardened productionized environment for serving the model.

Thanks.

True. No matter what their intention is, they are doing the right thing. I don't like that DL framework controlled by Google like what they do with Android. This would probably motivate Facebook/MSFT to develop a shared intermediate representation for DL frameworks, that would benefit so many people.

Whatever their incentives this hyper competition between companies is always good for the consumer. Same with iOS vs Android.