Abstract: To enable data-driven decision making, H&M is betting big on machine learning algorithms. As these algorithms proven extremely successful, our use cases needed to scale rapidly from a few countries to almost every country where H&M operates. With that, we needed to rethink how we orchestrate machine learning pipelines to train and serve a large number of new models in production on a regular basis. In this talk, we share how we designed, implemented, and operationalized a cloud-native scalable architecture for machine learning algorithms using Apache Airflow and Azure Kubernetes Service. About the speaker: Misbah Uddin