50 min
What is Data Science like at NVIDIA? With Meriem Bendris - What's AI Podcast Episode 1 What's AI Podcast by Louis-François Bouchard
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- Technology
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? 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?
50 min