KubeCon + CloudNativeCon India 2024

Flatcar is an operating system purpose-built for securely running container workloads. It ensures security by providing essential tools, reducing the OS size with minimal packages, and making the /usr directory immutable. This tutorial is designed for ops and DevOps professionals, offering step-by-step guidance on getting started with Flatcar. You’ll explore how to deploy it using Terraform, configure it with Ignition, and automate OS updates via Nebraska, enabling you to "set it and forget it.

The future of AI will be on cloud because of various reasons like scalability, computing power, features like Dynamic Resource Allocation and being able to run AI workloads across multiple clouds. However, the fields of cloud native and machine learning are still somewhat separated. We aim to bridge this gap with this tutorial. We plan on giving a brief introduction to machine learning with scikit-learn and cloud native with Kubernetes, then dive into a hands-on tutorial. We will show different ways to train and run ML models on and off the cloud (CPUs vs GPUs, public cloud vs on-prem, different flavors of Kubernetes) and evaluate performance in terms of training speed and cost. Additionally, training ML models is a complicated task and requires a lot of research. Optimizing your cloud setup on top of this makes it more difficult. There is no single solution, and we will explore a few of these options during this tutorial.

As AI becomes more popular, it's bringing some new headaches when it comes to managing models, from building them to getting them into production. The usual DevOps tools don't always cut it, especially when you need everything to be consistent and handle large datasets without a hitch. As ML projects grow, trying to manage everything manually just gets messy and prone to mistakes. In this talk, I'll break down how GitOps can make life easier by automating and simplifying ML operations. With Git as the go-to source for everything, GitOps keeps your ML models consistent and reproducible across different environments. I'll share some real-world examples, talk about the challenges, and give you practical tips on how to bring GitOps into your ML workflow. The goal is to help you deploy models faster and with fewer headaches.

We need to keep in mind many principles & techniques while writing a Kubernetes operator and especially so when used in production. Projects like kubebuilder go a long way to help in building operators. However, talks focus on how to write operators. But in this talk, you will learn how testing can help you create Kubernetes operators that are reliable and resilient to failure and give you the confidence to deploy them to production clusters. Often writing unit tests is not enough for an operator that manages infrastructure. Introducing e2e tests will help, but writing and running e2e tests are time-consuming. This session will share how a user can use envtest to simulate an environment very similar to a real environment and run the tests on behalf of that. In addition, you’ll learn what to keep in mind while writing the tests, such as it doesn’t support garbage collection, and a user must be very careful while creating objects and deleting them after a test suite is done.