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Copilots Everywhere: Thomas Dohmke and Eugene Yan
2024-07-26 · 17:35
Join GitHub CEO Thomas Dohmke for the closing keynote with a deep dive on Copilot Workspace, and what’s ahead as he talks on AI’s coming agentic wave. Recorded live in San Francisco at the AI Engineer World's Fair. See the full schedule of talks at https://www.ai.engineer/worldsfair/2024/schedule & join us at the AI Engineer World's Fair in 2025! Get your tickets today at https://ai.engineer/2025 About Thomas Fascinated by software development since his childhood in Germany, Thomas Dohmke has built a career building tools developers love and accelerating innovations that are changing software development. Currently, Thomas is Chief Executive Officer of GitHub, where he has overseen the launch of the world's first at-scale AI developer tool, GitHub Copilot. Before his time at GitHub, Thomas previously co-founded HockeyApp and led the company as CEO through its acquisition by Microsoft in 2014, and holds a PhD in mechanical engineering from University of Glasgow, UK. About Eugene I build ML systems to serve customers at scale, and write to learn and teach. |
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Lessons From A Year Building With LLMs
2024-07-19 · 17:41
Special double-feature closing keynote from the 6 authors of the hit O'Reilly article on Applied LLMs. Recorded live in San Francisco at the AI Engineer World's Fair. See the full schedule of talks at https://www.ai.engineer/worldsfair/2024/schedule & join us at the AI Engineer World's Fair in 2025! Get your tickets today at https://ai.engineer/2025 About Eugene Yan I build ML systems to serve customers at scale, and write to learn and teach. About Shreya Shankar I'm Shreya Shankar. I am a machine learning (ML) engineer and computer scientist in the Bay Area. I am completing my PhD in data management systems for ML, with a human-centered focus. I am fortunate to be advised by Dr. Aditya Parameswaran at UC Berkeley. Go Bears! 🐻 I also consult on ML engineering and production AI strategy for enterprises. Prior to my PhD, I was the first ML engineer at a startup, did research engineering at Google Brain, and engineering at Facebook. Before all of that, I did my BS and MS in computer science at Stanford. Go Trees! 🌲 About Hamel Husain Hamel Husain started working with language models five years ago when he led the team that created CodeSearchNet, a precursor to GitHub CoPilot. Since then, he has seen many successful and unsuccessful approaches to building LLM products. Hamel is also an active open source maintainer and contributor of a wide range of ML/AI projects. Hamel is currently an independent consultant. About Jason Liu Jason is an independent AI consultant, advisor, writer, and educator. His main interests are structured outputs, search and retrieval for RAG as well as understanding how to leverage AI to build scalable and valuable businesses. About Bryan Bischof Bryan Bischof is the Head of AI at Hex, where he leads the team of engineers building Magic—the data science and analytics copilot. Bryan has worked all over the data stack leading teams in analytics, machine learning engineering, data platform engineering, and AI engineering. He started the data team at Blue Bottle Coffee, led several projects at Stitch Fix, and built the data teams at Weights and Biases. Bryan previously co-authored the book Building Production Recommendation Systems with O’Reilly, and teaches Data Science and Analytics in the graduate school at Rutgers. His Ph.D. is in pure mathematics. About Charles Frye AI Engineer at Modal Labs. Building useful technology with large neural networks. 00:00 Introduction 03:22 Strategic: Bryan Bischof & Charles Frye 14:47 Operational: Hamel Husain & Jason Liu 23:51 Tactical: Eugene Yan & Shreya Shankar |
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Building Blocks for LLM Systems & Products: Eugene Yan
2023-11-02 · 13:08
“There is a large class of problems that are easy to imagine and build demos for, but extremely hard to make products out of. For example, self-driving: It’s easy to demo a car self-driving around a block, but making it into a product takes a decade.” - Andrej Karpathy This talk is about practical patterns for integrating large language models (LLMs) into systems and products. We’ll draw from academic research, industry resources, and practitioner know-how, and try to distill them into key ideas and practices. There are seven key patterns. I’ve also organized them along the spectrum of improving performance vs. reducing cost/risk, and closer to the data vs. closer to the user. Evals: To measure performance RAG: To add recent, external knowledge Fine-tuning: To get better at specific tasks Caching: To reduce latency & cost Guardrails: To ensure output quality Defensive UX: To anticipate & manage errors gracefully Collect user feedback: To build our data flywheel Recorded live in San Francisco at the AI Engineer Summit 2023. See the full schedule of talks at https://ai.engineer/summit/schedule & join us at the AI Engineer World's Fair in 2024! Get your tickets today at https://ai.engineer/worlds-fair About Eugene Yan Eugene Yan designs, builds, and operates machine learning systems that serve customers at scale. He's currently a Senior Applied Scientist at Amazon. Previously, he led machine learning at Lazada (acquired by Alibaba) and a Healthtech Series A. He writes & speaks about ML systems, engineering, and career at eugeneyan.com and https://ApplyingML.com |
AI Engineer Summit 2023 |
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DataTalks.Club Behind the Scenes - Eugene Yan, Alexey Grigorev
2022-01-21 · 18:00
Alexey Grigorev
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Eugene Yan
– guest
We talked about: Alexey’s background Being a principal data scientist DataTalks.Club The beginning and growth of DataTalks.Club Sustaining the pace Types of talks Popular and favorite talks Making DataTalks.Club self-sufficient Alexey’s book and course Advice for people starting in data science and staying motivated Not keeping up to date with new tools Staying productive Learning technical subjects and keeping notes Inspiration and idea generation for DataTalks.Club Links: https://eugeneyan.com/writing/informal-mentors-alexey-grigorev/ Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html |
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The Importance of Writing in a Tech Career - Eugene Yan
2021-01-15 · 22:04
Eugene Yan
– guest
We talk about blogging technical writing. We cover: Why should we write online? What should we write about? Writing at work: Design documents, wikis, etc. The writing process (also at work) Eugene's website: eugeneyan.com Follow Eugene on Twitter: https://twitter.com/eugeneyan Suggest topics: https://eugeneyan.com/topic-poll/ Join DataTalks.Club: https://datatalks.club |
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