Recent Articles on the tecRacer AWS Blog

The RAG - Retrieval Augmented Generation is an approach to reduce hallucination using LLMs (Large Language Models). With RAG you need a storage solution, which is a vector-store in most cases. When you have the task to build the infrastructure for such a use case, you have to decide which database to use. Sometimes, the best solution is not the biggest one. Then you should go serverless to a smaller solution, which fits the use-case better. In this post, I introduce some of the solutions and aid you in deciding which one to choose.

Many projects get to the point where your sophisticated infrastructure delivers reports to S3 and now you need a way for your end users to get them. Giving everyone access to the AWS account usually doesn’t work. In this post we’ll look at an alternative - we’re going to build a Serverless S3 Explorer with Dash, Lambda and the API Gateway.

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.

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.