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Advanced Event-Driven Patterns with Amazon EventBridge
Asynchronous communication and event-driven architecture are core to building distributed applications. Amazon EventBridge, a fully-managed serverless event bus, enables coordinating distributed and loosely coupled microservices to accomplish business functionality.
In this session, understand the characteristics of EventBridge and how it plays a pivotal role in serverless architectures. Learn the primary elements of event-driven architecture and some of the best practices.
With real-world use cases, explore how the features of EventBridge support implementing advanced architectural patterns in serverless.
In this session, understand the characteristics of EventBridge and how it plays a pivotal role in serverless architectures. Learn the primary elements of event-driven architecture and some of the best practices.
With real-world use cases, explore how the features of EventBridge support implementing advanced architectural patterns in serverless.
My AI Coding Partner: Building the London Improv Calendar on AWS
What happens when you treat AI like a coding partner? In this talk, I’ll share how I used AI tools to build the London Improv Calendar – a fully serverless application on AWS (Lambda, DynamoDB, API Gateway, and more).
This isn’t theory; it’s a practical, in-the-trenches account of working with AI as a solo engineer. We’ll cover what worked well, what fell flat, and where AI truly accelerated development.
If you’re curious about pairing AI with serverless, or just want some real-world lessons you can apply to your own projects, this session is for you.
This isn’t theory; it’s a practical, in-the-trenches account of working with AI as a solo engineer. We’ll cover what worked well, what fell flat, and where AI truly accelerated development.
If you’re curious about pairing AI with serverless, or just want some real-world lessons you can apply to your own projects, this session is for you.
From Prototype to Production with Strands Agents and Amazon Bedrock AgentCore
AI agents are a new class of software applications that use AI models to reason, plan, act, learn, and adapt in pursuit of user-defined goals with limited human oversight. Building AI agents that can reliably perform complex tasks has become increasingly accessible thanks to open source frameworks like Strands Agents. However, moving from a promising proof-of-concept to a production-ready agent that can scale to thousands of users presents significant challenges.
Through hands-on demos, we’ll build a system from scratch and progressively deploy it to production using the comprehensive enterprise-grade services provided by AgentCore.
You’ll learn to implement key production capabilities, including secure session isolation, persistent memory, identity management, and real-time observability. The learnings can be applied to any framework and model, hosted on Amazon Bedrock or elsewhere.
Through hands-on demos, we’ll build a system from scratch and progressively deploy it to production using the comprehensive enterprise-grade services provided by AgentCore.
You’ll learn to implement key production capabilities, including secure session isolation, persistent memory, identity management, and real-time observability. The learnings can be applied to any framework and model, hosted on Amazon Bedrock or elsewhere.
Backend in the Age of Serverless
Drawing from real-world projects, including MVPs and production apps built with AWS, I’ll share a practical decision-making framework for choosing your stack with a focus on architecture and AWS services.
How We Built an Ai-Powered Code Reviewer With Serverless and Bedrock
How hard can it be to build an AI-powered code reviewer using modern LLMs? Easier than you think – until it isn’t! Yan recently helped a client launch an AI-powered code reviewer. They built the initial prototype in the first few hours. But they soon encountered many unexpected challenges as they tried to productionize the idea into a coherent user experience.
In this session, let’s hear the surprises, challenges, and lessons learned from building an AI-powered code reviewer and what it can tell us about the current state of LLM technologies.
In this session, let’s hear the surprises, challenges, and lessons learned from building an AI-powered code reviewer and what it can tell us about the current state of LLM technologies.
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