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.
Hamel is an entreprenuer-in-residence at [fast.ai](https://github.com/fastai), where he is building new software development tools like [nbdev](https://github.com/fastai/nbdev). Prior to fast.ai, Hamel was a machine learning engineer at companies like Airbnb, GitHub, and DataRobot, and other related roles in management consulting. You can find more about Hamel on his [personal site](https://hamel.dev/).