LocalStack Resource Library

This is where it all comes together.CI/CD lets dev teams ship code automatically — but only if your pipeline is built to handle the cloud.In this episode, I show you how local testing + automated deployment = cloud apps that ship faster, safer, and smarter.Stick around to the end, this is the final episode of WTH is the Cloud?!

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.

Testing AWS CI/CD pipelines in the cloud can be slow, error-prone, and hard to debug, especially when you're wrestling with IAM permissions or waiting on long feedback cycles. This session walks through how you can now emulate complete DevOps workflows locally using LocalStack.We cover recent additions to LocalStack that support new service providers such as: CodeBuild: Run build processes across different runtimes directly on your machine CodeDeploy: Emulate deployment steps without touching the real infrastructure CodePipeline: Create and test CI pipelines, transitions, and triggers locallyThrough a live demo, we’ll walk through a working example of a CI/CD pipeline — building a Rust project, deploying it, and running the pipeline stages — all without leaving your laptop.This session is useful for developers building or debugging AWS-native CI/CD workflows and looking for faster, more controlled ways to test them.

Creating data pipelines and applications for the cloud comes with challenges like a complicated developer experience, dealing with managed cloud dependencies, and enduring long build times. These issues often disrupt your development and testing cycles.LocalStack's cloud emulation allows you to construct, deploy, and test data pipelines on your local machine. It facilitates integration testing of cloud solutions both locally and in CI pipelines. This approach saves time and money, enhances developer velocity, and supports high-quality, agile, test-driven development.In this talk, Harsh delves into developing and testing cloud-based data pipelines on your local machine. The session will provide a firsthand look at the new Snowflake emulator and demonstrate how you can use LocalStack to create Snowflake warehouses, databases, schemas, and tables, and integrate frameworks like Snowpark.

Building and debugging cloud-native applications often involves slow CI/CD pipelines, hard-to-reproduce bugs, and the need for costly shared environments. LocalStack offers a better way — letting developers simulate real AWS services entirely on their local machine.In this presentation, Kiah Imani gives a hands-on walkthrough of building and testing AWS workflows locally with LocalStack. From Lambda functions to S3 pre-signed uploads and SNS/SQS pipelines, you'll see how to prototype, debug, and iterate on cloud-native apps without ever deploying to the cloud.### Resources- S3: https://docs.localstack.cloud/aws/services/s3/- Lambda: https://docs.localstack.cloud/aws/services/lambda/- SQS: https://docs.localstack.cloud/aws/services/sqs/- SNS: https://docs.localstack.cloud/aws/services/sns/- Repo: https://github.com/localstack-samples/sample-serverless-image-resizer-s3-lambda/

Running AI/ML workloads in the cloud can be expensive, opaque, and difficult to iterate on. LocalStack changes this by enabling engineers to develop and test AI-powered cloud applications entirely locally, emulating services like SageMaker, Bedrock, Redshift, and Snowflake.In this presentation, Waldemar Hummer, CTO of LocalStack, demonstrates how to prototype and validate AI & ML data pipelines safely and cost-effectively using LocalStack’s cloud emulators. You’ll see how to emulate complex AI workflows, test integrations, and use “vibe coding” techniques confidently in a fully sandboxed local environment.

AWS Database Migration Service provides migration solutions from databases, data warehouses, and other types of data stores (e.g. S3, SAP). The migration can be homogeneous (source and target have the same type), but often is heterogeneous as it supports migration from various sources to various targets (self-hosted and AWS services).LocalStack supports DMS with selected use cases. In this session from LocalStack Community Meetup July '24, Mathieu Cloutier explores how to use LocalStack to migrate from a MariaDB database to an AWS Kinesis Stream. He goes over the differences between CDC and full load, and as a bonus you will see how easy it is to migrate from an external database to your Kinesis Stream — tested all on your local machine!Docs: https://docs.localstack.cloud/user-guide/aws/dms/

How much faster could your cloud application release cycles move if your developers didn’t need to deploy code to the cloud?

‍

Local cloud development eliminates the security implications, cost concerns, and access restrictions of traditional cloud development by replicating production-quality application environments on local infrastructure.

‍

Join us on Tuesday, December 16, at 1pm eastern time for a live demo webinar to learn more about:

• How LocalStack replicates production-quality AWS application environments on local infrastructure
• The unique advantages that local cloud environments provide for software developers
• Successful use cases where local cloud development unlocks velocity that developers can’t experience on the cloud

‍

Even if you’re not available to join the livestream, sign-up here to receive the session recording in your inbox.

‍

What if your software could fix its own bugs—before anyone even notices them? In this session, LogicStar co-founder Boris Paskalev shares how self-healing applications are becoming a reality—fixing bugs automatically, before they reach production or immediately after an issue is detected/reported. LogicStar combines classical computer science, deep tech research from the pioneers of “AI for Code” and Agentic AI to detect, reproduce, and fix real production issues with validated, test-backed pull requests.This session is for engineering leaders, PMs, and AI builders ready to rethink the boundaries of autonomy in software delivery.