Yuval Gorchover
ML Engineering Team Lead at Voyantis with extensive backend engineering experience across diverse tech companies.
I oversee ML technical initiatives, having transformed our ML cycle with a scalable SQL-based platform and built an advanced inference service for large-scale predictions. When not reimagining machine learning systems, I share insights through my publications on Towards Data Science.
Passionate about driving technical innovation in the data world.
Session
Production ML failures often stem from one overlooked issue: features that work perfectly in development break during inference. Through hands-on demonstration, this session shows how to eliminate feature drift using Feast's Python-based open source architecture. Learn to build reliable feature pipelines that maintain consistency across training and serving environments, ensuring your models perform as expected when deployed at scale.