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The two bottlenecks in the deployment of ML: - Availability of large quantities of labeled data - Availability of ML engineers Large companies have a decisive advantage on both fronts. This is why we're unlikely to see a new ML startup skyrocketing to a position of dominance
The good news is, we have managed to make compute a commodity (although not a cheap one), and to greatly reduce the need for specialists via frameworks like TensorFlow and Keras. Removing bottlenecks one at a time
just missing #data then
I think you are underestimating the availability of ML engineers. Skill sets needed are math, programming and a lot of motivation. I see it available in abundance.
This is brilliant. What would be very helpful is usage of tensorflow in non-image or non-language processing usecases. For example, a lot of Google's own usecases are built on logistic regression (like https://research.googleblog.com/2017/02/using-machine-learning-to-predict.html?m=1 …) so the usefulness seems very limited.
This makes a huge difference 
. Keras/Tensorflow + GCloud/AWS speeds up dev by a huge factor. I’d guess at least 10x compared to 10 years ago when I was first involved in a computer vision startup.
(now give us access to those TPUs 
)
Keras & TF are very helpful in getting a new app off the ground in no time. No question about that but if the ppl using the frameworks know nothing about the underlying algos, there could be huge issues down the road
On the other hand, application of ML in specialized domains depends on gathering that data, which is possible for all cos, large or small. For example, CRISPR or directed evolution in biotech
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