Edge computing: Centralizing services into data centers

Edge computing is computing that takes place near the physical location of either the user or the source of the data. By placing computing services closer to these locations, users benefit from faster, more reliable services and companies benefit from the flexibility of hybrid cloud computing. Edge computing also allows the compute-intensive parts of a rendering pipeline to be offloaded to the cloud, preventing lags in computing power.

Additional Edge computing resources

Kubeflow 1.0 monitoring and enhanced JupyterHub builds in Open Data Hub 0.8

Kubeflow 1.0 monitoring and enhanced JupyterHub builds in Open Data Hub 0.8

September 18, 2020

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 […]

Open Data Hub 0.7 adds support for Kubeflow 1.0

Open Data Hub 0.7 adds support for Kubeflow 1.0

August 13, 2020

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. Kubeflow 1.0 on OpenShift […]

From notebooks to pipelines: Using Open Data Hub and Kubeflow on OpenShift

From notebooks to pipelines: Using Open Data Hub and Kubeflow on OpenShift

July 29, 2020

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 […]

Developing at the edge: Best practices for edge computing

July 16, 2020

Edge computing continues to gain force as ever more companies increase their investments in edge, even if they’re only dipping their toes in with small-scale pilot deployments. Emerging use cases like Internet-of-Things (IoT), augmented reality, and virtual reality (AR/VR), robotics, and telecommunications-network functions are often cited as key drivers for companies moving computing to the […]

A development roadmap for Open Data Hub

A development roadmap for Open Data Hub

June 22, 2020

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 […]

Open Data Hub 0.6.1: Bug fix release to smooth out redesign regressions

Open Data Hub 0.6.1: Bug fix release to smooth out redesign regressions

June 2, 2020

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 […]

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