How to set-up and execute Kubeflow pipelines through DKube's JuypterLab notebook and how to manage or automate the pipeline runs through the UI. This is one of the hottest features of Kubeflow allowing you to set-up a multi-stage pipeline of data prep, feature engineering, training, and production deployment based on time triggers or other event triggers.
Machine Learning offers the promise of revolutionizing AI. But to achieve that promise, it needs to move from something that only a few experts can use to a mainstream discipline. Until recently, only experts could navigate the complex patchwork of tools and processes required to create such a system.
Over the past two plus years Kubeflow has come together a long way in bringing together many open source innovations to implement a cost effective AI/ML platform. These include KF Pipelines, KF Serving, AutoML, Istio from Kubernetes in addition to framework operators like Tensorflow, PyTorch, XGBoost, SciKit. However, “implementing” Kubeflow with your preferred cloud or on-prem environment still requires significant work still, with few people, for many months. The work comes in terms of:
There's a faster way to go from research to application. Find out how an MLOps workflow can benefit your teams.