DoK Talks #73- Build Reproducible Experiments with Kubeflow and lakeFS // Barak Amar

Kubeflow is a cloud-native ML platform that simplifies the training and deployment of machine learning pipelines on Kubernetes. lakeFS is wrapper layer around an object store that enables git-like operations such as branching and committing over datasets. Learn how to build ML workflows that are portable, scalable, and reproducible by integrating lakeFS operations into your Kubeflow pipeline components

Barak is a software developer of the open source lakeFS project at Treeverse. He has more than 15 years of experience in a wide range of systems, from scheduling to security. He’s passionate about three things: programming languages, distributed systems, and long-distance running.