What is Data Science like at NVIDIA? An interview with Meriem Bendris, Senior Solution Architect at NVIDIA.

Sign up to Meriem's free session: https://www.nvidia.com/gtc/session-catalog/?ncid=ref-inpa-477072&tab.catalogallsessionstab=16566177511100015Kus&search=meriem#/session/1670255843552001iaMr

The interview answers the questions...

00:00 Hey! Give a Thumbs up to the video If you enjoy it and let me know who or which role you’d like me to interview next! 

00:50 How did you get into NVIDIA? What’s your academic background? 

04:12 How were the NVIDIA  interviews? 

05:54 How did you prepare for the interviews? 

09:13 What is a solution architect at Nvidia? 

13:47 How are the rôles responsibilities at NVIDIA? 

17:15 Do you see any resemblance between your work at NVIDIA and when you were doing your PhD or postgraduate degree? 

23:10 When making models more efficients (quantizing), do you reduce performance significantly or do you manage to make them more efficient without sacrificing performance? 

25:10 What do you mean by distributing a model and why would you do that? 

29:43 Would you say that your PHD was worthwhile? 

33:25 How can someone coming from a completely different field make the transition into data science? 

40:00 Would you recommend diving into resource usage/management when learning AI? 

43:00 What material/hardware do you need when wanting to learn AI?