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.