Automated ECS deployments using AWS CodePipeline

When developing applications, particularly in the realm of containerization, CI/CD workflows and pipelines play an important role in ensuring automated testing, security scanning, and seamless deployment. Leveraging a pipeline-based approach enables fast and secure shipping of new features by adhering to a standardized set of procedures and principles. Using the AWS cloud’s flexibility amplifies this process, facilitating even faster development cycles and dependable software delivery. In this blog post, I aim to demonstrate how you can leverage AWS CodePipeline and Amazon ECS alongside Terraform to implement an automated CI/CD pipeline. This pipeline efficiently handles the building, testing, and deployment of containerized applications, streamlining your development and delivery processes.

Building Lambda with terraform

Note: This is an updated version of this blog. Building Lambda Functions with Terraform Introduction Many of us use Terraform to manage our infrastructure as code. As AWS users, Lambda functions tend to be an important part of our infrastructure and its automation. Deploying - and especially building - Lambda functions with Terraform unfortunately isn’t as straightforward as I’d like. (To be fair: it’s very much debatable whether you should use Terraform for this purpose, but I’d like to do that - and if I didn’t, you wouldn’t get to read this article, so let’s continue)

Deploying a Serverless Dash App with AWS SAM and Lambda

Today I’m going to show you how to deploy a Dash app in a Lambda Function behind an API Gateway. This setup is truly serverless and allows you to only pay for infrastructure when there is traffic, which is an ideal deployment model for small (internal) applications. Dash is a Python framework that enables you to build interactive frontend applications without writing a single line of Javascript. Internally and in projects we like to use it in order to build a quick proof of concept for data driven applications because of the nice integration with Plotly and pandas.

Understanding Iterations in Ray RLlib

Recently I’ve been engaged in my first reinforcement learning project using Ray’s RLlib and Sagemaker. I had dabbled in machine learning before, but one of the nice things about this project is that it allows me to dive deep into something unfamiliar. Naturally, that results in some mistakes being made. Today I want to share a bit about my experience in trying to improve the iteration time for the IMPALA algorithm in Ray’s RLlib.