Designing an ML model is one thing; designing an ML system that actually solves a business problem is another.

This talk explores how ML system design bridges the gap between a model and a real solution. Through practical examples, we’ll look at how communication with stakeholders, understanding functional and non-functional requirements, and aligning optimization and evaluation with business needs determine whether an ML initiative succeeds or stalls.

We’ll highlight key decision points — from translating vague goals into measurable objectives to balancing model performance with constraints like latency, interpretability, and maintainability.

Attendees will walk away with a sharper view of what makes an ML system truly fit for its environment — and why good design matters as much as good modeling.