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👋 Hi, I’m @hpfpv ☁️ I’m a Cloud Infrastructure Architect | 8x AWS Certified 🚀 I build secure, scalable, and automated solutions on AWS using Terraform, CloudFormation, and CI/CD 📚 Always exploring hybrid cloud, serverless, and AI-driven architectures
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Hi guys! So a few months ago I was helping out on a public-sector AWS Landing Zone, and one day the leadership team came to me with what sounded like a small request. They wanted one document that sum
TL;DR: I shipped a Bedrock Agent todo chatbot and then found I could read another user's todos via prompt injection. The fix that mattered most: promptSessionAttributes is a hint, not enforcement — re
A Bedrock Agent, a WebSocket, and the parts I had to figure out along the way
How we exposed AWS services to on-premises networks with overlapping CIDRs
A journey through the evolution of AI-Infrastructure integration
AWS re:Invent 2024 continues to be an incredible journey of learning, innovation, and a few surprises. Here's my recap of days 3 and 4, filled with technical insights, hands-on labs, and even some live entertainment.
The day started with an insightful workshop that showcased best practices and strategies - most of them relying on the use of GenAI or ML-powered services - to ensure workload stability, performance, and security while meeting organizational goals. Key points included:
To ensure high performance and scalability, it's important to test workloads thoroughly before they go live. Distributed Load Testing (DLT) makes this easier by automating large-scale tests to find and fix potential bottlenecks.
To ensure resilience, eliminate single points of failure and test system strength. AWS tools like Resilience Hub and Fault Injection Simulator help check recovery plans. For proactive monitoring, we can use services like Amazon DevOps Guru to detect and solve issues before they affect operations.
This session focused on designing and scaling Retrieval-Augmented Generation (RAG) systems with Amazon Bedrock. It covered practical strategies, including:
• Data Optimization: Effective preprocessing techniques to streamline data pipelines for RAG systems.
• Knowledge Base Integration: Using external knowledge sources to enrich and improve LLM-generated outputs.
• Scalable: Infrastructure design patterns for building resilient systems capable of handling the unique demands of RAG-based applications.
The addition of structured data retrieval in Bedrock Knowledge Bases aligns perfectly with RAG principles, simplifying the integration of complex datasets and enabling new possibilities for customer support, analytics, and content generation use cases.
Amazon announced a new family of foundation models called Amazon Nova, featuring four distinct models with different capabilities: Nova Micro - A text-only model with 128K context, Nova Light - A multimodal model for fast processing, Nova Pro - An advanced multimodal model, and Nova Premiere - The most capable model for complex reasoning.
This session highlighted Amazon’s Nova series of AI foundation models, showcasing their advanced intelligence, fast processing speeds, excellent cost-effectiveness, and, most importantly, easy integration with Bedrock.
Dr. Vogels delivered a standout keynote on designing for resilience and simplicity in modern architectures. Key insights:
Breaking Down Monoliths: Modular designs reduce complexity and allow for better fault isolation.
Building for Failure: Systems should not only withstand failures but also adapt and recover from them.
Simplicity in Innovation: Balancing innovation with operational simplicity ensures long-term scalability.
His talk was inspiring and packed with actionable advice, providing a clear roadmap for handling complexity in cloud-native systems.
This hands-on session was all about crafting better prompts to improve LLM responses. The steps involved:
Deploying a Single-Page Application: Using Amazon S3, AWS Lambda, and API Gateway for user interaction with Bedrock models.
Experimenting with Prompts: Testing and fine-tuning prompts in an Amazon SageMaker notebook.
Observing the Impact: Witnessing how small changes to prompts can significantly enhance the output quality.
This lab showed how important prompt design is for getting the most out of generative AI. It was also my first experience with AWS SimuLearn, where you converse with an AI chatbot to identify technical and business requirements, and then transition to a self-paced lab to deploy and test the solution.
This session focused on creating intelligent tools using Amazon Bedrock. Key activities included:
Agent Configuration: Actions Groups, linking APIs, and associating Lambda functions to create actionable agents.
Testing and Deployment: Running scenarios to test agent behavior and deploying them for real-world use.
Practical Integration: Techniques for embedding agents into existing workflows to improve automation and efficiency.
It was a highly interactive session that showed how intelligent agents could drive innovation in business processes.
The day ended with the re:Play Party, a chance to unwind and have some fun. The highlight? Seeing Weezer perform live!
I also tried my hand at an RC car game and nearly won. These lighter moments added a personal touch to what has been an intellectually intense event.
Days 3 and 4 at AWS re:Invent have been packed with learning and hands-on practice. From exploring the latest AI designs to understanding the nuances of operational resilience, each session has left me with valuable insights. And, of course, the Replay Party was a perfect way to balance work and play.
As my time at re:Invent 2024 comes to the end, I’m grateful for the opportunity to learn, connect, and grow in this dynamic environment.