I agree with the central thesis: engineering is a huge bottleneck. I work for a FinTech company that is building novel machine learning models and this is our experience.
We've had a few machine learning experts working here for a couple of years, but recently brought in a software engineer with a passion for machine learning. He was able to, within a few months, streamline the data acquisition pipeline to the point where we could iterate on a new models in about 30 minutes, down from days. He accomplished this not just with better data but by building efficient in-memory data structures. It saves literally days of time per iteration because of disk I/O.
Before his work the training data versus the data we used in production had minor differences. Each new release required intensive manual verification to make sure that our model worked. Now we have much more certainty that the two match up.
Looking down on engineering problems is like a famous architect looking down on structural engineers. You're not gonna have a very good skyscraper if your foundation is shaky and ad-hoc.
We've had a few machine learning experts working here for a couple of years, but recently brought in a software engineer with a passion for machine learning. He was able to, within a few months, streamline the data acquisition pipeline to the point where we could iterate on a new models in about 30 minutes, down from days. He accomplished this not just with better data but by building efficient in-memory data structures. It saves literally days of time per iteration because of disk I/O.
Before his work the training data versus the data we used in production had minor differences. Each new release required intensive manual verification to make sure that our model worked. Now we have much more certainty that the two match up.
Looking down on engineering problems is like a famous architect looking down on structural engineers. You're not gonna have a very good skyscraper if your foundation is shaky and ad-hoc.