Welcome to this technical series on getting long-term value from Machine Learning (ML) with Kubeflow on Lightbend Platform with Red Hat OpenShift.

In Part 1 of this series “How To Deploy And Use Kubeflow On Red Hat OpenShift”, we discussed what Kubeflow is, and how it can be useful for running Machine Learning applications in production. In this post, we discuss installation of Kubeflow.

In Part 2 of “How To Deploy And Use Kubeflow On OpenShift”, we looked at Installation and some of the additional components used by Kubeflow, such as Ambassador, Spartakus, Argo, Minio, etc. In this post, we will describe and show how to use some of them.

In Part 3 of “How To Deploy And Use Kubeflow On OpenShift”, we looked at Kubeflow support components like Argo, Ambassador, Minio, and Spartakus. In this post, we look at using JupyterHub.

In Part 4 of “How To Deploy And Use Kubeflow On OpenShift”, we looked at setting up and using JupyterHub with Kubeflow. In this post, we look closer at running Machine Learning jobs with TensorFlow.

In Part 5 of “How To Deploy And Use Kubeflow On OpenShift”, we looked at machine learning in production. In this part, we turn our attention to tuning ML parameters with Kubeflow and Katib.

In Part 6 of “How To Deploy And Use Kubeflow On OpenShift”, we examined Kubeflow ML parameters tuning. In this part, we look at serving ML models in production.

In Part 7 of “How To Deploy And Use Kubeflow On OpenShift”, we looked at model serving with Kubeflow. In this part, we now look at deploying Kubeflow pipelines.

In the last article in “How To Deploy And Use Kubeflow On OpenShift”, we looked at deployment operations using Kubeflow pipelines. In this final part of the series, we look at model management as the last component of Kubeflow that we will describe, ModelDB.

In this two-part blog series, we introduce the concepts and benefits of working with both spark-submit and the Kubernetes Operator for Spark. In Part 1, we introduce both tools and review how to get started monitoring and managing your Spark clusters on Kubernetes. In Part 2, we do a deeper dive into using Kubernetes Operator for Spark.

In the first part of this blog series, we introduced the usage of spark-submit with a Kubernetes backend, and the general ideas behind using the Kubernetes Operator for Spark. In this second part, we are going to take a deep dive in the most useful functionalities of the Operator, including the CLI tools and the webhook feature. At the end, we review the advantages and disadvantages of both spark-submit and Operator.

In this special guest webinar with Holden Karau, speaker, author and Developer Advocate at Google, we’ll take a walk through some of the interesting Spark 3.0 JIRA tickets, look at external components being developed (like deep learning support), and also talk about the future of running real-time Spark workloads on Kubernetes.

The Lightbend engineering teams have released Lightbend Orchestration 1.7.1., Lagom 1.4.11, and sbt 1.3.18 for your immediate use. Enjoy!

In this webinar with special guest Fabio Tiriticco, we will explore how Akka is the perfect companion to Kubernetes, providing the application level requirements needed to successfully deploy and manage your cloud-native services with technologies built specifically for cloud-native applications, like Kubernetes.

Lightbend CTO and Co-Founder, Jonas Bonér, releases a new white paper describing how to use Akka and Kubernetes for cloud-native applications.

The Fast Data team at Lightbend is happy to announce the release of Fast Data Platform version 1.2, adding Kubernetes support plus new features.

In this webinar by Sean Glover (@seg1o), Lightbend Senior Engineer for Cloudflow, we go through some best practices that we've learned working with Kafka, DC/OS, and Kubernetes with Lightbend clients.