The hardest task for a senior engineer is to learn a new area where they will need to become a beginner again. But at some point, this is the only way to grow, and doing it successfully unlocks unique impact. Especially true for great engineers switching into machine learning.

May 30, 2022 · 4:14 PM UTC

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Replying to @gdb
Amen! It's been a 7 yr journey for this 61 yr old C/UNIX sw engineer. I've learned Kubernetes, Docker, Python, git, Tensorflow, Keras, Horovod, CUDA, and the whole model architecture zoo. No regrets!
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Replying to @gdb
True. I guess this can be applied to more than engineering.
GIF
Replying to @gdb
And another one is understanding that the architecture you know may not be the best choice for a current project.
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Replying to @gdb
Where does basic Supply and Demand fit into ML?
Replying to @gdb
I actually enjoy being a beginner because the resources on that level are plentiful and high quality as opposed to research level where they're sparse, less organised and not yet processed enough by the community to be easily digestible.
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Replying to @gdb
In my experience, it is quite difficult to move into the machine learning field as a software engineer if you also have to change companies to do so, because you lack professional exp. Even if you apply for junior positions. This limits your growth opportunities significantly.
Replying to @gdb
So much to learn and be excited about in ML! As more engineers/devs jump into ML, I think ML is going to see a massive consolidation of roles where data engineers, data scientists and ML engineers combine into a full-stack data scientist enabled by better ML building blocks.
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Replying to @gdb
Learning new mnemonics can sometimes be an added problem but a ‘journeyman’ engineer can often integrate new information quite readily even when new concepts are involved. It depends how skillfully they are explained.
Replying to @gdb
Agreed
Replying to @gdb
True. Not just for developers, but any senior in any domain
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