2025-04-25 –, Europium2
Scaling machine learning pipelines is no small feat - especially when you’re managing over 100 of them on AWS SageMaker. In this talk, I’ll take you behind the scenes of how our team at idealo built a Git-based MLOps framework that powers millions of real-time recommendations every minute.
I’ll share the challenges we faced, the solutions we implemented, and the lessons we learned while streamlining model versioning, deployment, and monitoring. This session is packed with actionable takeaways for ML engineers, data scientists, and DevOps professionals looking to simplify their MLOps workflows and operate efficiently at scale.
Whether you’re running a handful of pipelines or preparing to scale up, this talk will equip you with the tools and strategies to tackle MLOps with confidence.
In 2022, idealo’s Machine Learning Engineering (MLE) team took on a bold mission: to transform and scale the recommendation systems powering the idealo website. Fast forward to today, we’re delivering over 1 million recommendations per minute across 20 key user touchpoints - driving seamless, personalized experiences at scale.
But how do you manage over 100 machine learning pipelines without breaking a sweat? In this talk, I’ll reveal the three core principles that helped us build a sustainable and efficient MLOps workflow in AWS SageMaker:
- Decoupling pipeline releases from deployments for ultimate flexibility
- Testing pipelines to ensure seamless performance
- Centrally managing infrastructure as code for full control and scalability
If you’re ready to supercharge your MLOps game, this session will leave you with practical strategies and battle-tested solutions for running ML pipelines like a pro.
Advanced
Expected audience expertise: Python:Novice
Bogdan Girman is an expert in Machine Learning and DevOps, with extensive experience in implementing scalable, reproducible ML systems. He is passionate about bridging the gap between development and operations in AI.