Room 2 - Breakout

In this session I talk about all things that can be represented as code: starting from the very basic software and ending modern approaches such as infrastructure as code and observability as code

As software developers, we’re all guilty of having at least a few unfinished side projects collecting dust on our GitHub repositories. For me, blogging was one of those ideas that stayed on my to-do list for ages until AWS Amplify changed the game. In this session, I’ll share how I used AWS Amplify to stop procrastinating and finally build a platform to share content focused on helping women and tech beginners navigate their tech journeys. We’ll explore how Amplify makes it easy to deploy frontend apps, add storage, connect to real-time data sources, and scale seamlessly. The best part? All of that without needing deep cloud expertise. Join me as I walk through my journey from idea to execution with AWS Amplify and learn how you can do the same to bring your projects to life, breaking free from procrastination.

Examples of functionless (a.k.a. Lambda-less) integration pattern, where a direct integration between two AWS services is configured instead of using custom code in an intermediary Lambda function, based on Step Functions, AppSync, EventBridge.

Cloud cost optimization isn’t about clever billing tricks, there are no shortcuts here. It’s about engineering maturity. If you’re serious about lowering your cloud spend without breaking your velocity, join this presentation. We’ll look at why savings plans and tools aren’t enough, how finance and engineering often misalign, and what high-maturity teams do differently. You’ll leave knowing what to focus on and where the real impact lies.

Building RAG-powered agents doesn’t have to come with sky-high vector database costs. In this session, learn how to create an Amazon Bedrock Agent backed by Amazon S3 Vectors, a new low-cost vector storage option that cuts vector storage and query costs by up to 90%.

We’ll walk through:

1. Setting up a Bedrock Knowledge Base using S3 Vectors for semantic retrieval
2. Creating a Bedrock Agent that can reason over a large-scale knowledge base
3. Designing embeddings, chunking, and metadata filtering strategies for accurate results

Whether building internal copilots, customer support bots, or document search, this talk shows how to do RAG at scale without the price tag of high-performance vector databases. This is ideal for developers and teams looking to build practical, budget-friendly GenAI apps on AWS.