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Behind the Scenes : The Challenges on Building AI Applications

Posted Oct 31, 2023 | Views 367
# AI Applications
# Foundational Models
# Lepton AI
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Yuze Ma
Founding Product Manager @ Lepton AI

Yuze currently serves as the founding product manager at Lepton AI, a pioneering force in simplifying AI application development. Before Lepton AI, he worked at Alibaba Cloud, where he masterminded AI platforms across high-performance computing infrastructures. Under his leadership, his teams birthed products from inception to generating hundreds of millions in revenue. Prior to this, he made waves at Codemao as a data scientist, orchestrating the integration of AI into production and leading impactful projects. He also contributed to a renowned data science tool, Project Jupyter, which is used by millions of data scientists around the world.

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Adam Becker
IRL @ MLOps Community

I'm a tech entrepreneur and I spent the last decade founding companies that drive societal change.

I am now building Deep Matter, a startup still in stealth mode...

I was most recently building Telepath, the world's most developer-friendly machine learning platform. Throughout my previous projects, I had learned that building machine learning powered applications is hard - especially hard when you don't have a background in data science. I believe that this is choking innovation, especially in industries that can't support large data teams.

For example, I previously co-founded Call Time AI, where we used Artificial Intelligence to assemble and study the largest database of political contributions. The company powered progressive campaigns from school board to the Presidency. As of October, 2020, we helped Democrats raise tens of millions of dollars. In April of 2021, we sold Call Time to Political Data Inc.. Our success, in large part, is due to our ability to productionize machine learning.

I believe that knowledge is unbounded, and that everything that is not forbidden by laws of nature is achievable, given the right knowledge. This holds immense promise for the future of intelligence and therefore for the future of well-being. I believe that the process of mining knowledge should be done honestly and responsibly, and that wielding it should be done with care. I co-founded Telepath to give more tools to more people to access more knowledge.

I'm fascinated by the relationship between technology, science and history. I graduated from UC Berkeley with degrees in Astrophysics and Classics and have published several papers on those topics. I was previously a researcher at the Getty Villa where I wrote about Ancient Greek math and at the Weizmann Institute, where I researched supernovae.

I currently live in New York City. I enjoy advising startups, thinking about how they can make for an excellent vehicle for addressing the Israeli-Palestinian conflict, and hearing from random folks who stumble on my LinkedIn profile. Reach out, friend!

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SUMMARY

As foundational models move fast in size and capability, the gap between research to production is yet to be completed. To iterate through the models and implementations faster, the toolchain matters a lot. This talk will cover the techniques and experience during this journey from the training of the vanilla foundational models, and optimization comparisons, to user-side system experience optimization.

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