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
AI software stack inspection with Thoth and TensorFlow
September 30, 2020Project Thoth develops open source tools that enhance the day-to-day life of developers and data scientists. Thoth uses machine-generated knowledge to boost the performance, security, and quality of your applications using artificial intelligence (AI) through reinforcement learning (RL). This machine-learning approach is implemented in Thoth adviser (if you want to know more, click here) and […]
Kubeflow 1.0 monitoring and enhanced JupyterHub builds in Open Data Hub 0.8
September 18, 2020The 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
August 13, 2020Open 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
July 29, 2020Data 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, 2020Edge 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
June 22, 2020Open 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 […]
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July 18, 2019