2025-11-05 –, Eng
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.
Feature inconsistency is the silent killer of production ML systems. While data scientists perfect models in notebooks, real-world predictions fail because features differ between training and serving.
Feast (feast.dev), the rapidly growing open-source feature store, solves this by creating a bridge between data engineering and machine learning.
This session explores Feast's architecture that solves key ML challenges: feature consistency, point-in-time correctness, and low-latency serving. Through a churn prediction example, attendees will see how Feast's registry and store design handles both historical training data and real-time predictions.
Participants will learn to implement a production-grade feature management system that scales with their ML operations.
Previous knowledge expected
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.