Data science requires a different type of copilot than software development. But current tools are not fit for purpose. How can you use AI-assisted development to expedite your work, produce better models and more production-ready pipelines?

Discussion on AI-assisted development for data science, tooling evolution, safety, fairness, and compliance in regulated industries. Examples draw from healthcare; references to open-source libraries and standards such as LangTest, HELM, NIST, CHAI, and ISO.

Raluca Crisan, CTO, etiq.ai — Coding assistants are not quite enough for Data science or ML work, they don’t quite solve the right kind of problems. However they are undeniably helpful. In this talk we explain how combining coding assistants and elements from MLOps can unlock immense potential value for the space.