In this Lightbend Podcast, I sit down with Dean Wampler, VP of Fast Data Engineering, and Craig Blitz, Senior Product Director, of Lightbend. The topic: Lightbend Pipelines, our newest module to become part of Lightbend Platform.

Introducing Kafka Lag Exporter, an OSS tool created by Lightbend's Sean Glover to make it easy to view consumer group metrics in Kafka using Kubernetes, Strimzi, Prometheus, Grafana, and other tools.

Lightbend today announced the general availability of Lightbend Pipelines, a new, comprehensive system that accelerates all lifecycle aspects of streaming data pipelines and integrating them with microservices-based applications.

In this webinar with Lightbend's Craig Blitz, Product Director, and Kiki Carter, Principal Enterprise Architect, we review how Lightbend’s Pipelines module enables you to develop components ("streamlets") using the appropriate technology, wire them together as pipelines, and deploy them with Kubernetes without all the manual, time-consuming labor.

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.