I’ll teach you how to add interactive basic auth to the Serverless Dash app that we deployed recently.

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)

This guide empowers you to optimize OpenSearch for lightning-fast and accurate phone number searches. Frustration-free experiences are key for your customers, and by leveraging edge ngrams and custom analyzers, you can empower OpenSearch to efficiently handle even large datasets.

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.

A few years ago Amazon SageMaker introduced direct support for reinforcement learning (RL) through integration of RL-frameworks, including Ray. However, support has not been kept up to date and the supported versions are no longer what you might call current.

A webserver running on a container. Sound simple. Let`s dive deeper into how your architecture choices affect application security. I use docker scout for the container and show how Amazon Inspector can serve as a general-purpose security tool.

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.

In part one, we took the journey from a POC monolith to a scaleable two-tier architecture. The focus is on the DevOps KPI deployment time and the testability. With the right tools - AWS SAM and Postman - the dirty work becomes a nice walk in the garden again. See what a KEBEG stack can achieve!