MLOps Coffee Sessions #175 with Lamia Youseff, From Virtualization to AI Integration.

// Abstract
Lamia discusses how both Fortune 500 companies and SMBs lack the knowledge and capabilities to identify which use cases in their systems can benefit from AI integration. She emphasizes the importance of helping these companies integrate AI effectively and acquire the necessary capabilities to stay competitive in the market.

// Bio
By way of an introduction, Dr. Lamia Youseff has been working in AI / ML for ~25 years, first in academia (MIT, Stanford, UCSB), then large tech (Google, Microsoft, Apple, and Facebook), and most recently with startups in Generative AI. She is currently the executive director of JazzComputing, a Visiting Research Scientist at Stanford University in Computer Science and AI, and a research affiliate with MIT Computer Science and Artificial Intelligence Lab (CSAIL). Dr. Youseff earned her Ph.D. in computer science by studying computationally intensive workloads (such as AI / ML and HPC / Scientific Codes) and has built/led several AI teams as an executive and leader at large tech companies over the years (Google, Facebook, Microsoft, and Apple). She also earned her Master's in business management, strategy, and leadership from Stanford Graduate School of Business (GSB), where she is a guest lecturer today. Dr. Youseff regularly writes and speaks about AI and Machine Learning evolution at CIO/CTO/CEO summits.

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Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with Lamia on LinkedIn: https://www.linkedin.com/in/lyouseff/

Timestamps:
[00:00] Lamia's preferred coffee
[01:12] Takeaways
[03:00] Please like, share, and subscribe to our MLOps channels!
[03:20] Lamia's background
[09:52] Getting into Google Cloud
[13:10] The Google Cloud project
[16:38] The world before Kubernetes
[19:25] Evolution of virtualization
[23:20] Cloud evolution
[28:13] Kubernetes enables the ecosystem
[32:38] Multiple systems for machine learning
[34:40] Standardization to a greater good
[39:50] Complexity and pain points of ML in production
[46:26] JazzComputing
[50:33] Bridging gaps in AI implementation and investment
[51:19] Wrap up