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.

Cloud infrastructure has fueled innovation for nearly two decades—yet cost control remains a challenge. Unforeseen expenses and complicated billing can hamper agility, forcing teams to overspend just to stay competitive.What if you could evaluate costs in real-time, identify inefficiencies, and optimize deployments—without slowing development? Imagine adjusting parameters based on on-the-fly estimates and usage.In this presentation, Malcolm Matalka, Co-founder and CTO of Terrateam, explores how OpenInfraQuote, a new open-source command-line tool, transforms Terraform and OpenTofu code into actionable cost insights. Learn how to automate price checks, compare scenarios, and avoid financial surprises—alongside how it differs from other solutions and how to integrate it into your workflow.Resources- GitHub: https://github.com/terrateamio/openinfraquote- Documentation: https://openinfraquote.readthedocs.io/en/latest/

Multi-account and multi-region compatibility enables users to manage and deploy resources across multiple AWS accounts and geographic regions. This functionality enhances the robustness of the deployments by offering improved fault tolerance, scalability, and regulatory compliance. By segregating resources into separate accounts and distributing them across various regions, users can minimize the impact of potential failures and optimize performance.In this session from LocalStack Community Meetup May '24, Sannya Singhal discussed how you could use LocalStack to emulate multi-account and multi-region environments locally for testing and development purposes, ensuring that applications were resilient and scalable before deployment to the cloud.

LocalStack's cloud emulator lets you run Amazon Elastic Container Service (ECS) clusters and tasks on your local computer. It's sometimes useful to mount code from the host filesystem directly into the ECS container. This helps quickly test changes without needing to rebuild and redeploy the ECS Task's Docker image each time.This video explains how to use code mounting with the ECS bind mounts feature. Here are the links to the resources mentioned in the video:• Sample repository: https://github.com/localstack-samples/ecs-code-mounting-python-cdk• LocalStack Docs: https://docs.localstack.cloud/user-guide/aws/ecs/#mounting-local-directories-for-ecs-tasks• AWS Docs: https://docs.aws.amazon.com/AmazonECS/latest/developerguide/bind-mounts.html