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The way in which Google released TensorFlow (and the meteoric success it's enjoyed) is an excellent example of a company successfully commoditizing its compleme
by dsacco 9y ago
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.
- nicodjimenez 9y agoTwo 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
- dsacco 9y agoWhy do you believe "Tensorflow sucks", and why do you believe PyTorch or Caffe2 is better, and for what metrics?
- ActsJuvenile 9y agoTwo 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.
- chronic732 9y agoThis 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.
- jamesblonde 9y agoJumbo 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.
- visarga 9y agoToday I learned: "commoditize your complements". Quite interesting compact way to put it.
- arithma 9y agoIt apparently isn't a new strategy, and is in the shared internet mind sphere. https://www.joelonsoftware.com/2002/06/12/strategy-letter-v/ https://www.joelonsoftware.com/2002/06/12/strategy-letter-v/
- dsacco 9y agoAs another commenter mentioned, I can't take credit for it. I first read it from Joel's blog.