Abstract: In recent years, advances in data science have made tremendous progress yet designing large-scale data science and machine learning applications still remain challenging. The variety of machine learning frameworks, hardware accelerators, cloud-vendors as well as the complexity of data science workflows brings new challenges to MLOps. It’s non-trivial for data scientists to easily launch, manage, monitor, and optimize their pipelines in a scalable way. On the other hand, Kubernetes and containerization have revolutionized cloud applications in a manner not seen since Linux and virtualization's disruption of the server market. In this talk, we’ll provide an overview of the existing tools available and best practices to do MLOps effectively in the cloud-native era.
Bio: Yuan is currently a founding engineer at Akuity. Previously he was a senior software engineer at Alibaba Group, building AI infrastructure and AutoML platform. He's a PMC member of XGBoost and Apache MXNet, co-chair of Kubeflow, maintainer of TensorFlow, Argo, Couler, and ElasticDL, as well as author of numerous open source projects. He's the author of Distributed Machine Learning Patterns (https://github.com/terrytangyuan/distributed-ml-patterns) as well as the co-author of TensorFlow in Practice and Dive into Deep Learning (with TensorFlow).