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

The machine learning life cycle, Part 1: Methods for understanding data

The machine learning life cycle, Part 1: Methods for understanding data

May 11, 2021

I think of machine learning as tools and technologies that help us find meaning in data. In this article, we’ll look at how understanding data helps us build better models. This is the first article in a series that covers a simple life cycle of a machine learning project. In future articles, you’ll learn how […]

Kubernetes configuration patterns, Part 1: Patterns for Kubernetes primitives

Kubernetes configuration patterns, Part 1: Patterns for Kubernetes primitives

April 28, 2021

This article is the first in a two-part article series on Kubernetes configuration patterns, which represent ways of configuring Kubernetes applications and controllers. Part 1 introduces simple approaches that use only Kubernetes primitives. These patterns are applicable to any application running on Kubernetes. Part 2 will introduce more advanced patterns. These patterns require you to […]

4 reasons you'll love using Red Hat OpenShift Data Science

4 reasons you'll love using Red Hat OpenShift Data Science

April 27, 2021

Red Hat OpenShift Data Science is a managed cloud service built from a curated set of components from the upstream Open Data Hub project. It aims to provide a stable sandbox in which data scientists can develop, train, and test their machine learning (ML) workloads and then deploy results in a container-ready format. This article […]

Managing Python dependencies with the Thoth JupyterLab extension

Managing Python dependencies with the Thoth JupyterLab extension

March 19, 2021

JupyterLab is a flexible and powerful tool for working with Jupyter notebooks. Its interactive user interface (UI) lets you use terminals, text editors, file browsers, and other components alongside your Jupyter notebook. JupyterLab 3.0 was released in January 2021. Project Thoth develops open source tools that enhance the day-to-day lives of developers and data scientists. […]

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 […]

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