Building a Scalable and reliable open source ML Platform with MLFlow

MLFlow has been widely used as a reliable ML experiment tracking and model registry to pave the way towards MLOps. However, its flexibility also imposes challenges on having a robust and consistent way working across different scenarios. In this talk we will have a closer look at how to properly use MLFlow in combination with other open source frameworks such as Airflow and Kubernetes to further mature the lifecycle of machine learning use cases.