Recent Articles on the tecRacer AWS Blog

It’s probably not a surprise that (almost) everything Amazon Web Services offers is accessible through web services. AWS also provides a range of SDKs that make interfacing with and integrating them in your code base relatively painless. These SDKs are sometimes a bit limited, though. There are certain things that you can only do through the AWS console, meaning that there are still services under the surface, but they’re undocumented and not accessible through the official SDKs. That API call is what we refer to as an undocumented API. In this post, I’m going to show you how to discover these APIs and use them in your scripts.

In the most recent installment of the Serverless Dash series, we’ll look at implementing proper authentication using Cognito. This post covers setting up the user pool, the app client and also the logout option.

Finding the right set of hyperparameters when training machine learning models is a resource-consuming and costly task. In this post, we try to simplify this by exploring hyperparameter tuning for reinforcement learning models with Ray 2.x on AWS Sagemaker.

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)