Linkedin, Spotify, Volvo, JP Morgan, and many other market leaders are leveraging Kubeflow to simplify the creation and the efficient deployment of Machine Learning models on Kubernetes. This presentation will provide an update on the Kubeflow 1.0 release, and review the Community’s best practices to support Critical User Journeys, which optimize ML workflows. As a data scientist will often need to build (and save) hundreds of variants of their model, this session will provide a deeper dive into how an integrated storage solution simplifies model-building and increases ML productivity. The presentation will examine how to optimize the daily workflows of data scientists, and eliminate complex and time-consuming manual tasks. The talk will also highlight how efficient Kubeflow operations rely on Kubernetes storage primitives, such as Dynamic Volume Provisioning, Persistent Volumes and StatefulSets. This integrated solution simplifies the configuration, operations and data protection for Kubeflow and generic K8s stateful apps in production-grade, multi-user environments.
In this episode
Kubeflow Community Product Manager and VP , Arrikto
Josh Bottum is a Kubeflow Community Product Manager. His Community responsibilities include assisting users to quantify Kubeflow business value, develop critical user journeys (CUJs), triage incoming user issues, prioritize feature delivery, write release announcements and deliver Kubeflow presentations and demonstrations. Mr. Bottum is also a VP of Arrikto. Arrikto simplifies storage operations for stateful Kubernetes applications by enabling efficient local storage architectures with data durability and portability. Arrikto is a core code contributor to Kubeflow.
Demetrios is one of the main organizers of the MLOps community and currently resides in a small town outside Frankfurt, Germany. He is an avid traveller who taught English as a second language to see the world and learn about new cultures. Demetrios fell into the Machine Learning Operations world, and since, has interviewed the leading names around MLOps, Data Science, and ML. Since diving into the nitty-gritty of Machine Learning Operations he felt a strong calling to explore the ethical issues surrounding ML. When he is not conducting interviews you can find him making stone stacking with his daughter in the woods or playing the ukulele by the campfire.