AI/ML: Persistent workspaces for multiple users
The first challenge for an AI/ML practitioner is gathering the necessary data to feed the process. The solution? Advanced planning algorithms that organize data better than humans in far less time.
This article is the second in a two-part article series on Kubernetes configuration patterns, which you can use to configure your Kubernetes applications and controllers. The first article introduced patterns and antipatterns that use only Kubernetes primitives. Those simple patterns are applicable to any application. This second article describes more advanced patterns that require coding […]
Even though cloud-native computing has been around for some time—the Cloud Native Computing Foundation was started in 2015; an eon in computer time—not every developer has experienced the, uh, “joy” of dealing with distributed systems. The old patterns of thinking and architecting systems have given way to new ideas and new problems. For example, it’s […]
As a developer, I’m always excited to attend the Kafka Summit, happening this year from May 11 to 12. There are so many great sessions addressing critical challenges in the Apache Kafka ecosystem. One example is how changes to event-driven APIs are leading developers to focus on contract-first development for Kafka. In preparation for the […]
Embracing the future—making the transition from legacy monolithic applications running on .NET Framework to microservices and images running in containers (or pods)—is a tall task. If only there were a safe, proceed-at-your-own-pace way to make the change, one that was familiar yet led to a new destination. Of course, there is such a path; otherwise, […]
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 […]
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 […]