AWS Atlas

Organizations are accelerating innovation with generative AI and agentic AI use cases. This session explores how AWS streaming and messaging services such as Amazon Managed Streaming for Apache Kafka, Kinesis Data Streams, Amazon Managed Service for Apache Flink, and Amazon SQS build intelligent, responsive applications.

Training large AI models requires significant compute resources and can be time and cost intensive. In this session, learn to optimize and accelerate your model training workloads using AWS's purpose-built infrastructure and tools. We'll dive deep into leveraging services like Amazon SageMaker HyperPod for distributed training at scale and SageMaker fully managed training jobs for cost-effective ML acceleration.

AWS Graviton-based Amazon EC2 instances provide the best price performance for workloads in Amazon EC2. In this session, dive deep into the AWS Graviton processor and learn about its workload performance, energy efficiency, and software offerings. Graviton5 processor, our newest Graviton processors. Learn about common use cases, best practices to optimize your workloads across various applications, customer success stories, and how you can accelerate your AWS Graviton journey.

Physical AI is changing robotics by enabling systems to interact intelligently with the world, but development requires extensive compute, simulation, and deployment resources. This session shows how NVIDIA’s Isaac platform combined with AWS allows end-to-end implementation: robots are trained and validated in high-fidelity simulated environments using Amazon EC2 GPU instances, with large-scale workloads orchestrated via AWS Batch and Isaac Lab for parallel experiments.

What if you could deploy a production-ready containerized application with just one command? Amazon ECS Express Mode makes this possible. In this lightning talk, watch a live demonstration of deploying a containerized application that's instantly production-ready with automatic URL generation, scaling, and AWS best practices built-in. Learn how you can start simple and still access the full power of ECS as your application needs grow.

Legacy applications run mission-critical processes, but increasingly limit innovation and organizational agility through outdated architectures. This session equips leaders with frameworks to refactor applications into cloud-native solutions while preserving business operations. Successful modernization requires answering strategic questions: why modernize, at what cost, how to modernize, what risks exist, and when to act.

Discover how Ericsson leverages AWS AI solutions to revolutionize mobile networks through two groundbreaking use cases. First, explore Ericsson's journey toward Autonomous Network AI multi-agents and reinforcement learning solutions, automating RAN optimization, cutting operational effort by up to 50% while dynamically improving network coverage, capacity, and quality. Second, witness Ericsson's solution to knowledge de-fragmentation in complex telecom development.

Building autonomous AI at scale with Amazon Bedrock 2025 marks the breakthrough year for autonomous AI systems, from vibe coding to specialized assistants and super agents disrupting multi-billion dollar industries. Amazon Bedrock empowers disruptive startups to rapidly prototype autonomous applications and agents, seamlessly scaling from proof-of-concept to production systems serving millions of users.

AI coding agents are transforming how developers build and operate modern cloud-native applications. With tools such as Kiro CLI, Kiro IDE, or any MCP-compatible AI coding assistant, developers are embracing AI to move faster and scale smarter. This session explores how Model Context Protocol (MCP) servers for AWS services like ECS and EKS provide real-time, contextual code assistance to AI-powered coding agents.

Discover how organizations build robust and scalable data foundations that power AI agents, automation, and BI analytics at enterprise scale. Join a customer as they share how they modernize their analytics environment with Amazon Quick Suite, enabling governed, high-performance insights across their business. Learn best practices for designing reliable data pipelines and architectures that support conversational AI, analytical agents, and business intelligence workloads.

As serverless adoption grows, teams need consistent patterns for security, observability, governance, and CI/CD to avoid duplicating efforts and architectural inconsistencies.This session explores serverless platform engineering through a lens of architectural blueprints: a structured approach that promotes consistency while empowering teams to focus on core business value.

Learn how to build scalable, autonomous AI workflows using the Agent-to-Agent (A2A) protocol and Model Context Protocol (MCP). This session demonstrates how to design specialized agents that dynamically discover each other's capabilities and collaborate on complex tasks across platforms and implementations. Using MCP for standardized context integration, you'll build horizontally scalable, serverless agents with AWS Lambda, Amazon Bedrock, and Momento Cache.