CI/CD

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

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

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 data monitoring that is best captured or automated using pipelines. This brings us to a dilemma: How do you move code from notebooks to containers orchestrated in a pipeline, and schedule the pipeline to run after specific triggers like time of day, new batch data, and monitoring metrics?

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What enterprise developers need to know about security and compliance

What enterprise developers need to know about security and compliance

One of the luxuries of my job is that I get to speak to and work with a range of IT people employed by U.S. federal and state government agencies. That range includes DevOps engineers, developers, sysadmins, database administrators, and security professionals. Everyone I talk to, even security professionals, says that IT security and compliance can be imprecise, subjective, overwhelming, and variable—especially in the federal government.

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Enterprise Kubernetes development with odo: The CLI tool for developers

Enterprise Kubernetes development with odo: The CLI tool for developers

Kubernetes conversations rarely center the developer’s perspective. As a result, doing our job in a k8s cluster often requires building complicated YAML resource files, writing custom shell scripts, and understanding the countless options that are available in kubectl and docker commands. On top of all of that, we have the learning curve of understanding Kubernetes terminology and using it the way that operations teams do.

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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

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 experience with ODH 0.6 does not suffer because we wanted to release early, we offer a new (mostly) bugfix release: Open Data Hub 0.6.1.

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How the fabric8 Maven plug-in deploys Java applications to OpenShift

How the fabric8 Maven plug-in deploys Java applications to OpenShift

The fabric8 Maven plug-in, often abbreviated FMP, can be added to a Maven Java project and takes care of the administrative tasks involved in deploying the application to a Red Hat OpenShift cluster. These tasks include:

  1. Creating an OpenShift build configuration (BC).
  2. Coordinating the source-to-image (S2I) process to create a container image from the application’s compiled bytecode.
  3. Creating and instantiating a deployment configuration (DC) from information in the project.
  4. Defining and instantiating OpenShift services and routes.

All of the relevant components of this process are well-documented individually. This article pulls together documentation sources to create an overview of how the plug-in works, and the structure of the image it generates—which might make the plug-in easier to use and troubleshoot.

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Getting started with the fabric8 Kubernetes Java client

Getting started with the fabric8 Kubernetes Java client

Fabric8 has been available as a Java client for Kubernetes since 2015, and today is one of the most popular client libraries for Kubernetes. (The most popular is client-go, which is the client library for the Go programming language on Kubernetes.) In recent years, fabric8 has evolved from a Java client for the Kubernetes REST API to a full-fledged alternative to the kubectl command-line tool for Java-based development.

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Deploy and bind enterprise-grade microservices with Kubernetes Operators

Deploy and bind enterprise-grade microservices with Kubernetes Operators

Deploying enterprise-grade runtime components into Kubernetes can be daunting. You might wonder:

  • How do I fetch a certificate for my app?
  • What’s the syntax for autoscaling resources with the Horizontal Pod Autoscaler?
  • How do I link my container with a database and with a Kafka cluster?
  • Are my metrics going to Prometheus?
  • Also, how do I scale to zero with Knative?

Operators can help with all of those needs and more. In this article, I introduce three Operators—Runtime Component Operator, Service Binding Operator, and Open Liberty Operator—that work together to help you deploy containers like a pro.

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