Versioning your ML steps with Kubeflow

How does versioning your Machine Learning steps work with Kubeflow and Arrikto? For our 8th MLOps community meetup Josh Bottum VP of Arrikto and Kubeflow community product manager answers this question for us. This is taken from a longer conversation you can find here: https://youtu.be/jXRbj5xnBy4 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 chat we sit down with Josh Bottum, 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.