The new Open Data Hub version 0.8 (ODH) release includes many new features, continuous integration (CI) additions, and documentation updates. 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. In this article, we introduce the highlights of this newest release.
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.
Continue reading “Kubeflow 1.0 monitoring and enhanced JupyterHub builds in Open Data Hub 0.8”
Open Data Hub (ODH) is a blueprint for building an AI-as-a-Service (AIaaS) platform on Red Hat OpenShift 4. Version 0.7 of Open Data Hub includes support for deploying Kubeflow 1.0 on OpenShift, as well as increased component testing on the OpenShift continuous integration (CI) system. This article explores the recent updates.
Continue reading Open Data Hub 0.7 adds support for Kubeflow 1.0
Data scientists often use notebooks to explore data and create and experiment with models. At the end of this exploratory phase is the product-delivery phase, which is basically getting the final model to production. Serving a model in production is not a one-step final process, however. It is a continuous phase of training, development, and data monitoring that is best captured or automated using pipelines. This brings us to a dilemma: How do you move code from notebooks to containers orchestrated in a pipeline, and schedule the pipeline to run after specific triggers like time of day, new batch data, and monitoring metrics?
Continue reading From notebooks to pipelines: Using Open Data Hub and Kubeflow on OpenShift
The main goal of Kubernetes is to reach the desired state: to deploy our pods, set up the network, and provide storage. This paradigm extends to Operators, which use custom resources to define the state. When the Operator picks up the custom resource, it will always try to get to the state defined by it. That means that if we modify a resource that is managed by the Operator, it will quickly replace it to match the desired state.
Continue reading Open Data Hub and Kubeflow installation customization
Open Data Hub (ODH) is a blueprint for building an AI-as-a-Service (AIaaS) platform on Red Hat’s Kubernetes-based OpenShift 4.x. The Open Data Hub team recently released Open Data Hub 0.6.0, followed up by a smaller update of Open Data Hub 0.6.1.
We recently got together and discussed our plans and timeline for the next two releases. Our plans are based on the roadmap slide deck that we put together and presented during the Open Data Hub community meeting on April 6.
In this article, we present our roadmap for the next several Open Data Hub releases. We would like to emphasize that the target dates are optimistic, describing what we would like to achieve. With the current state of the world and vacation time coming up, these dates might change.
Continue reading “A development roadmap for Open Data Hub”
It is just a few short weeks since we released Open Data Hub (ODH) 0.6.0, bringing many changes to the underlying architecture and some new features. We found a few issues in this new version with the Kubeflow Operator and a few regressions that came in with the new JupyterHub updates. To make sure your experience with ODH 0.6 does not suffer because we wanted to release early, we offer a new (mostly) bugfix release: Open Data Hub 0.6.1.
Continue reading Open Data Hub 0.6.1: Bug fix release to smooth out redesign regressions
Open Data Hub (ODH) is a blueprint for building an AI-as-a-service platform on Red Hat’s Kubernetes-based OpenShift 4.x. Version 0.6 of Open Data Hub comes with significant changes to the overall architecture as well as component updates and additions. In this article, we explore these changes.
From Ansible Operator to Kustomize
If you follow the Open Data Hub project closely, you might be aware that we have been working on a major design change for a few weeks now. Since we started working closer with the Kubeflow community to get Kubeflow running on OpenShift, we decided to leverage Kubeflow as the Open Data Hub upstream and adopt its deployment tools—namely KFdef manifests and Kustomize—for deployment manifest customization.
Continue reading “Open Data Hub 0.6 brings component updates and Kubeflow architecture”
Open Data Hub is an open source project providing an end-to-end artificial intelligence and machine learning (AI/ML) platform that runs on Red Hat OpenShift. As we explained in our previous article, we see real potential and value in the Kubeflow project, and we’ve enabled Kubeflow 0.7 on RedHat OpenShift 4.2. Kubeflow installs multiple AI/ML components and requires Istio to control and route service traffic.
As part of the Open Data Hub project, we’ve also integrated Kubeflow with Red Hat OpenShift Service Mesh. In this article, we present Red Hat OpenShift Service Mesh as an alternative to the native Kubeflow Istio installation, especially for users who already have OpenShift Service Mesh installed on their cluster.
Continue reading “Integrating Kubeflow with Red Hat OpenShift Service Mesh”
As part of the Open Data Hub project, we see potential and value in the Kubeflow project, so we dedicated our efforts to enable Kubeflow on Red Hat OpenShift. We decided to use Kubeflow 0.7 as that was the latest released version at the time this work began. The work included adding new installation scripts that provide all of the necessary changes such as permissions for service accounts to run on OpenShift.
Continue reading Installing Kubeflow v0.7 on OpenShift 4.2
When it comes to the process of optimizing a production-level artificial intelligence/machine learning (AI/ML) process, workflows and pipelines are an integral part of this effort. Pipelines are used to create workflows that are repeatable, automated, customizable, and intelligent.
An example AI/ML pipeline is presented in Figure 1, where functionalities such as data extract, transform, and load (ETL), model training, model evaluation, and model serving are automated as part of the pipeline.
Continue reading “AI/ML pipelines using Open Data Hub and Kubeflow on Red Hat OpenShift”