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.

LocalStack integrates with official AWS Software Development Kits (SDKs) so you can connect to LocalStack services using the same application code you use for AWS services. This lets you develop and test your applications locally without connecting to the cloud.In this video, we will talk about how you can connect to LocalStack emulated services using AWS SDKs.

LocalStack is ephemeral, so when you stop and restart it, all data is lost. You can use certain features to save the state & load it back when you restart LocalStack. This includes saving the local state for S3 buckets, DynamoDB tables, RDS databases and more. In this video, we explore three mechanisms that allows you to save state in LocalStack. They are:• Persistence• State Export & Import• Cloud Pods ## Documentation• State management: https://docs.localstack.cloud/user-guide/state-management/ • Cloud Pods: https://docs.localstack.cloud/user-guide/state-management/cloud-pods/ • Persistence: https://docs.localstack.cloud/user-guide/state-management/persistence/ • State Export & Import: https://docs.localstack.cloud/user-guide/state-management/export-import-state/

In this video, you'll learn how to set up and integrate LocalStack's Snowflake Emulator to develop and test your Snowflake data apps in your local environment or CI pipelines. Whether you're using Snowpark, various client libraries, or building interactive data apps with frameworks like Streamlit, this emulator simplifies your developer experience.We'll walk you through step-by-step instructions on:- Installing the Snowflake emulator with the LocalStack CLI & Docker- Configuring and integrating the emulator with popular SQL clients, such as DBeaver- Running SQL queries locally to replicate a full Snowflake environment without cloud dependencies⚡ Get early access! The Snowflake Emulator is currently in public preview—reach out via the link below for access and start building today!## Resources- LocalStack for Snowflake documentation: https://snowflake.localstack.cloud/- LocalStack for Snowflake samples: https://github.com/localstack-samples/localstack-snowflake-samples- Get access: https://www.localstack.cloud/contact