Kubernetes ensures your deployed applications are always available to users. But how do you deploy applications in Kubernetes without user/service interruptions? Should you write your own scripts using low-level Kubernetes objects, package everything in Helm, or use specific CI/CD tools? There isn’t a clear-cut answer; it always depends.

Enabling support for data processing, data analytics, and machine learning workloads in Kubernetes has been one of the goals of the open-source community. During this meetup, we’ll discuss the growing use of Kubernetes for data science and machine learning workloads. We’ll examine how new Kubernetes extensibility features such as custom resources and custom controllers are used for applications and frameworks integration. Apache Spark 2.3.’s native support is the latest indication of this growing trend. We’ll demo a few examples of data science workloads running on Kubernetes clusters setup by our Kublr platform.

Is advanced scheduling in Kubernetes achievable? Yes, however, how do you properly accommodate every real-life scenario that a Kubernetes user might encounter? How do you leverage advanced scheduling techniques to shape and describe each scenario in easy-to-use rules and configurations?

Containers and Kubernetes allow for code portability across on-premise VMs, bare metal or multiple cloud provider environments. Yet, despite this portability promise, developers may include configuration and application definitions that constrain or even eliminate application portability. In this online meetup Oleg Chunikhin, CTO at Kublr, describes best practices for “configuration as code” in a Kubernetes environment. He demonstrates how a properly constructed containerized app can be deployed to both Amazon and Azure using the Kublr platform, and how Kubernetes objects, such as persistent volumes, ingress rules, and services, can be used to abstract from the infrastructure.

Curious to learn how Kublr works? This overview video is a great place to start. We'll walk you through the main features that the Kublr container management platform offers and how your team can benefit from it.

Enabling support for data processing, data analytics, and machine learning workloads in Kubernetes has been one of the goals of the open source community. During this meetup we’ll discuss the growing use of Kubernetes for data science and machine learning workloads. We’ll examine how new Kubernetes extensibility features such as custom resources and custom controllers are used for applications and frameworks integration. Apache Spark 2.3.’s native support is the latest indication of this growing trend. We’ll demo a few examples of data science workloads running on Kubernetes clusters setup by our Kublr platform.

Interfaces of advanced scheduling in Kubernetes are all over the place. They can be found in metadata objects, nodes and pods specs. In some scenarios, you may even have to touch Kubernetes configuration. Learn more from Oleg, Kublr's CTO.

Kubernetes considers several factors when scheduling pods and nodes. These include volumes, volume location, resource availability, and user-defined constraints. Hear more from Oleg, Kublr's CTO.