Good software practices ensure high-quality, maintainable, and scalable code. They reduce technical debt, improve collaboration, and make production deployments far less painful.

In data science, the focus often sits on model accuracy. But deploying a model means the entire pipeline, including data I/O, cleaning, and monitoring, must meet production standards. Designing with best practices from the start avoids costly rewrites later.

This talk covers modularity, separation of concerns, testability, and reproducibility. We'll use Kedro, an open-source Python framework, to illustrate how these principles work in practice, and the goal is for you to walk away with ideas you can apply directly in your own projects.

Beyond traditional ML, these software engineering principles extend naturally to modern AI techniques like Graph RAG and agentic reflection. Managing complexity and reproducibility becomes even more critical in non-deterministic agentic systems, and a solid project foundation makes that manageable regardless of your tooling.

Attendees will leave with practical insights applicable to any data project, regardless of their software engineering background.