arnabgho

"LLM evangelists - are you willing to admit that you just might not be that good at programming computers?"

The people who were the best at something don't necessarily be the best at a new paradigm. Unlearning some principles and learning new ones might be painful exercise for some masters.

Military history has shown that the masters of the new wave are not necessarily the masters of the previous wave we see the rise and downfall of several civilizations from Roman to Greek for being too sure of their old methods and old military equipments and strategy.

Anthropic: Chief Product Officer of OpenAI

I was a MLE Tech Lead at Snap and laid some of the foundations of the generative AI infra at Snap. I would highly recommend MLE route as a very rewarding career path.

This book is a very good introduction to designing Machine Learning Systems for production: https://www.amazon.com/Designing-Machine-Learning-Systems-Pr...

This blog by the same author is highly recommended as an intro into building production grade AI and ML systems: https://huyenchip.com/2023/04/11/llm-engineering.html

To summarize answers to your questions:

(1) Yes it is wise to do this transition especially at an inflection point in the zeitgeist of the times as now

(2) Yes

(3) See above for resources on how to get started and reach mastery in the craft of ML Engineering.

I have used the vybe app to varying degrees of success https://app.thevybeapp.com/