![]() ![]() Elyra lets you execute this process from the JupyterLab portal with just a few clicks. The process of converting all of the work that a data scientist has created in notebooks to a production-level pipeline is cumbersome and usually manual. In an effort to allow data scientists to turn their notebooks into Argo Workflows or Kubeflow pipelines, we've added an exciting new tool called Elyra to Open Data Hub 0.8. We've also updated Open Data Hub 0.8 with Elyra, an AI toolkit that lets you launch JupyterLab images. For more comprehensive information about Thoth and how to use it, visit Thoth Station. ![]() Notebook images are now built and maintained by Thoth Station, which is an artificial intelligence (AI) tool that analyzes and recommends software stacks for artificial intelligence applications. We also streamlined all of the images used by JupyterHub by pulling them from different private and public repositories into two repositories on Quay.io: odh-jupyterhub and thoth-station. Moving forward, all code changes and feature enhancements for JupyterHub will go under these two new repositories. In an effort to streamline code changes to JupyterHub, we forked two pivotal repositories under the opendatahub-io GitHub project: jupyterhub-quickstart and jupyterhub-odh. Note: Open Data Hub is an open source project and a community Operator for building an AI-as-a-Service (AIaaS) platform on Red Hat OpenShift. In this article, we introduce the highlights of this newest release. For this release, we focused on enhancing JupyterHub image builds, enabling more mixing of Open Data Hub and Kubeflow components, and designing our comprehensive end-to-end continuous integration and continuous deployment and delivery (CI/CD) process. The new Open Data Hub version 0.8 (ODH) release includes many new features, continuous integration (CI) additions, and documentation updates. ![]()
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