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?
What is Data Science like at NVIDIA? With Meriem Bendris (and RTX 4080 giveaway!)