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.