While developers see and realize the benefits of Kubernetes, how it improves efficiencies, saves time, and enables focus on the unique business requirements of each project; InfoSec, infrastructure, and software operations teams still face challenges when managing a new set of tools and technologies, and integrating them into existing enterprise infrastructure. 

Digital transformation may be in danger of becoming an overused buzzword. Yet, real business needs are driving this trend and IT leaders feel the pressure to transform their businesses every day. Whether it is the need for speed, agility, or rethinking business processes as a whole – these challenges are here to stay.

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.

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.