KubeCon + CloudNativeCon India 2024

As organizations scale, robust disaster recovery becomes critical. As clusters multiply and complexities grow, maintaining consistent RPO and RTO becomes challenging, requiring new techniques for efficient large-scale data backup and recovery. While GitOps excels at maintaining the desired state of infrastructure using Git as the single source of truth, it falls short for stateful workloads like databases and S3 buckets. Tools like Crossplane help deploy these resources in a GitOps model, but disaster recovery remains complex. You can't just spin up a new K8s cluster, point it to a Git repo, and expect a seamless recovery of critical stateful data Disaster recovery isn't 'one size fits all', each scenario requires a different approach depending on the scale of the disaster. In this talk, we’ll explore different strategies for handling different levels of disasters, from losing a single K8s cluster to facing a full region outage, helping folks to be better prepared for the unexpected.

Among the 4C (Cloud, Cluster, Container, Code) security in Kubernetes, there are various techniques to enhance the security of the cluster surface. In particular, Admission Control (webhook) is one of the most flexible and powerful methods. As this trend, there is movement to apply it to various forms of Kubernetes(e.g. GKE, Openshift and so on). In my opinion, one of the easiest and most efficient ways to apply it is to improve security through CEL (Common Expression Language). I believe that the Validating Admission Policy becoming `stable` in v1.30 is part of this proof. So I will show you the CEL DEMO provided by Google Cloud to get a quick and easy understanding of how to improve the security of GKE. Through this exercise, you will learn the basic structure of CEL and the freedom of scope that can be applied, and you will be able to apply it to any other platform with minimal effort.

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.

Machine learning platforms aim to streamline the workflow for ML practitioners, allowing them to focus on developing their models while the platform handles repetitive tasks like packaging code, dependencies and configurations. Traditional methods using Dockerfiles require ML engineers to navigate complex Linux processes and maintain multiple Dockerfiles for different projects, which can be time-consuming and prone to errors. Additionally, security mandates for regular patching and updates adding further to the complexity. Join this talk to explore how Cloud Native Buildpacks can simplify and secure MLOps deployments. By automating the packaging of ML projects, including custom libraries and hardware specifications, Buildpacks enhance flexibility, maintainability and security. This approach reduces the operational burden on both developers and security teams, ensuring a more efficient and scalable MLOps deployment process

Explore AI innovation with the "Generative AI Demo using Open Data Hub," an open-source AI/ML platform tailored for Kubernetes. This demo features two Mistral-based LLMs, one utilizing Retrieval-Augmented Generation (RAG) with PostgreSQL vector extensions, and another without. Users interact through a Gradio chatbot, demonstrating how RAG improves accuracy. Two persona-based pipelines (Admin & Data Scientist) highlight Open Data Hub’s seamless integration with Tekton-powered OpenShift Pipelines, showcasing the Hybrid Cloud Strategy. Key components include the Kubernetes Platform, Node Feature Discovery, NVIDIA GPU Operators, Software Defined Storage, Tekton, Argo CD (for GitOps), Gitea Operator, Knative and Istio. Join us to see how GPU-accelerated AI and scalable, secure infrastructure power next-gen applications.