Kubernetes is today’s hottest way to deploy and manage applications in the cloud, but it also offers the essential foundation for repeatable and reliable machine learning workflows. In this session, I will demonstrate open source tools that build on Kubernetes to facilitate solving data science workflow challenges for practitioners, without forcing data scientists to care about the primitive details of their infrastructure.
You’ll leave this talk with an understanding of how Kuberenetes supports data scientists at each step of the machine learning workflow. You’ll be introduced to high-level tools that effortlessly provision custom research environments, publish reproducible notebooks, operationalize models and pipelines as services, and detect data drift automatically.