KubeCon + CloudNativeCon India 2024

Have you felt left out of Kubernetes development because you're not a GoLang expert? The good news is you don’t need to be. While GoLang has traditionally been the go-to language for creating Kubernetes-native tools like Helm, Argo CD, and Prometheus, Kubernetes is language-agnostic and can be interacted with via its declarative REST API in any language. This session will explore the growing ecosystem of Kubernetes application development beyond Go. We’ll review popular Kubernetes libraries and frameworks in languages like Python, JavaScript, and Java. To illustrate, we’ll demo the Java Operator SDK, showcasing how to build Kubernetes applications without relying on Go. Whether you code in Python, JavaScript, or Java, this talk will give you the tools and confidence to develop Kubernetes-native applications in your preferred language.

While anyone working with Kubernetes knows about Controllers and Custom Resources, most don't know that there are many ways of designing a Controller, but just one good practice. That practice is "single resource": your controller should only manage a single resource, instead of having a single controller for multiple resources or multiple controllers for a single resource. This talk guides you on implementation of single-resource Controllers, showcases examples of a well-designed controller and good practice of designing Single-Resource controllers by comparing it with discouraged alternative designs. This talk focuses on practical insights, on aforementioned design’s pros and cons, and how its implementation affects the efficiency of Operators. After the talk, attendees will be able to design and implement single-resource controller, and have a deeper understanding of good practices of designing a kubernetes controller

The rapidly evolving landscape of Machine Learning and Large Language Models demands efficient scalable ways to run distributed workloads to train, fine-tune and serve models. Ray is an Open Source framework that simplifies distributed machine learning, and Kubernetes streamlines deployment. In this introductory talk, we'll uncover how to combine Ray and Kubernetes for your ML projects. You will learn about: - Basic Ray concepts (actors, tasks) and their relevance to ML - Setting up a simple Ray cluster within Kubernetes - Running your first distributed ML training job

We, Priyanka and Jason, have been involved in Kubernetes release cycles for years, taking on roles from shadow to Release Lead and Emeritus Advisor. While we've worked with Kubernetes Enhancement Proposals (KEPs) throughout this time, it wasn’t until the 1.29 release, while prepping for press interviews, that we really dug into them—and wow, did we learn a lot! KEPs aren’t just feature proposals; they give you a peek into the entire lifecycle of a feature—designs, alternatives, testing, and final code. They’re a goldmine for understanding Kubernetes, whether you’re new or a seasoned contributor. We’ll walk you through some real KEPs and share our approach to reading and making sense of them. If you’re curious about Kubernetes and want to explore its features more deeply, join us. There’s so much to discover, and we’re excited to share what we’ve learned with you!