Machine Learning Consultant, Parlance Labs
Hamel was previously an entrepreneur-in-residence at fast.ai, where he built software development tools like nbdev. Currently, he runs Parlance Labs as an Independent Consultant helping companies build AI products, serves on the R&D team at AnswerAI, and scouts for Bain Capital. With over 20 years of experience, Hamel has worked as a machine learning engineer at companies like Airbnb, GitHub (where he contributed to Copilot), and DataRobot. You can find more about Hamel on his personal site or his company site at parlance-labs.com.
Hamel Husain challenges the common misconception that having a powerful foundation model equals having a successful AI product, drawing from his extensive experience building AI systems at companies like GitHub, Airbnb, and DataRobot. This talk examines the crucial gap between model capabilities and product success, exploring the engineering, user experience, and business considerations that determine whether AI applications actually solve real problems for users.
We have seen a rapid explosion in tools in the machine learning and data ecosystem. These tools cover an expansive surface area, which includes orchestration, observability, experiment tracking, data quality, and more. Despite the proliferation of tools, one activity core to everyone’s workflow has been largely ignored: writing and distributing software. In this talk, Hamel will discuss innovative approaches and tools for software development, their history, and future directions. Hamel will discuss the historical threads upon which these new approaches are built, and discuss nbev, a popular open-source project that implements many of these ideas. Finally, Hamel will share learnings from building nbdev, along with challenges and future directions.
The next edition is programmed by practitioners. Tell us what you built and what you learned.
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