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

Knowledge meets machine learning for smarter decisions, Part 2

Knowledge meets machine learning for smarter decisions, Part 2

January 22, 2021

Red Hat Decision Manager helps organizations introduce the benefits of artificial intelligence to their daily operations. It is based on Drools, a popular open source project known for its powerful rules engine. In Part 1 of this article, we built a machine learning algorithm and stored it in a Predictive Model Markup Language (PMML) file. […]

Knowledge meets machine learning for smarter decisions, Part 1

Knowledge meets machine learning for smarter decisions, Part 1

January 14, 2021

Drools is a popular open source project known for its powerful rules engine. Few users realize that it can also be a gateway to the amazing possibilities of artificial intelligence. This two-part article introduces you to using Red Hat Decision Manager and its Drools-based rules engine to combine machine learning predictions with deterministic reasoning. In […]

Use Kebechet machine learning to perform source code operations

Use Kebechet machine learning to perform source code operations

December 24, 2020

One of the first tools we developed to help us with Project Thoth was Kebechet, which we named for the goddess of freshness and purification. As we separated our software into more and more repositories (each of our Python modules is in its own repository on GitHub), we needed help with releasing new versions and […]

AI software stack inspection with Thoth and TensorFlow

AI software stack inspection with Thoth and TensorFlow

September 30, 2020

Project 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

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 […]

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