Dmitry Levashov
I have a strong mathematical background and more than 10 years of experience working with data. Over the years, my focus has been on various applications of ML models, research to support decision-making, and personalizing customer experiences for projects with millions of users.
Currently, I'm Sr. Data & AI Specialist at Maxeda DIY Group, where I focus on advancing scalable AI solutions and integrating machine learning into business operations.
Session
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.