The challenge with Machine Learning (ML) models is productionizing. It requires data ingestion, data preparation, model training, model deployment, and monitoring. Adopting MLOps practices is similar to DevOps practices. In MLOps, the workload changes, but some core principles like automation, continuous integration/continuous deployment (CI/CD), and monitoring. Taking DevOps practices, I will discuss the similarities and differences in adopting MLOps practices. In this talk, Chinmay takes a production use case to scale ML models to 2 million+ daily requests. It leverages Google Cloud's (GCP) infrastructure to use its GPU and other services. This talk will help you draw similarities between DevOps and MLOps as a DevOps practitioner and help you learn how to run Machine Learning models at the production scale with best practices.

Ever wonder why some teams intentionally break their own systems? Welcome to the world of chaos engineering — a practice that's not just for Netflix-scale infrastructure, but for any team that wants to build resilient, reliable applications. In this session, we'll demystify chaos engineering and explain why intentionally breaking things is actually the smart move. You'll learn what chaos engineering really is (in plain English, no buzzwords), why waiting for production failures is a terrible strategy, how to start experimenting with controlled failure locally, before it happens in the wild, real-world examples of chaos experiments that catch bugs you'd never find in traditional testing, and tools and techniques to get started without blowing up your infrastructure. Through practical demos using LocalStack's cloud emulation and chaos engineering tools, we'll simulate failures like network latency, service outages, and resource exhaustion right from your laptop. If you've ever said "it worked on my machine" only to watch it crash in production, this talk is for you—let's break things intentionally so they don't break unexpectedly.

Testing in the cloud = slow builds, fragile staging, surprise bills. Let’s talk about how developers are flipping the script and using local cloud environments to test smarter, faster, and cheaper — without breaking production. Bonus: You’ll learn how LocalStack lets you simulate AWS on your machine. Game changer.

LocalStack now provides enhanced support for running AWS services in Kubernetes environments. In this presentation from the LocalStack 4.0 community meetup by Simon Walker, we explore how to deploy and manage local AWS resources within Kubernetes clusters with LocalStack, to help developers maintain consistency between development and production environments. The session further covers LocalStack’s Kubernetes integration, including deployment via Helm charts, configuration of services like Lambda and RDS as Kubernetes pods, and networking between components. A demo illustrates provisioning a serverless application (Lambda functions interacting with a MySQL database) using Terraform, with all resources managed within a local Kubernetes cluster. You'll additionally learn the practical approaches for local testing and infrastructure emulation by moving from Docker to Kubernetes-native solutions as well as upcoming features, including broader service support and new container runtime options.