Offers strategies for preparing people, processes, and governance to adopt GenAI-enabled DevOps at scale.

Discusses how AI can enhance security, compliance, and reliability in AWS-based DevOps environments.

Showcases real-world use cases of Generative AI for developers and operations teams to improve productivity, reliability, and speed.

Exploration of practical GenAI use cases for developers and operations teams in DevOps workflows.

Explores how Amazon Q accelerates DevOps workflows by enabling AI-powered automation and improved orchestration within the DevOps lifecycle.

Discussion on how Amazon Q changes the DevOps workflow by enabling AI-powered automation and orchestration in the development lifecycle.

Outlines the technical foundations and key components required to implement smarter automation across DevOps pipelines.

Overview of the technical foundations enabling smarter automation in modern DevOps with GenAI.

A generative AI gateway is a design pattern popular in enterprise settings which establishes a central gateway where developers can access foundation models from multiple providers. It includes features for access control, quota management, cost control, governance, and observability. This session will dive deep on a recommended architecture for building an AI gateway on AWS and provide a demo of the final result.

Expect a hands-on journey showing how modern data lake tools and governance platforms connect the dots - making your data discoverable, governed, and productized for real-world use.

EBMT, one of the biggest medical registries in Europe, has rebuilt its core data system from scratch, after 20 years of service, to keep up with growing data needs, modern technologies, and the evolving needs of researchers in blood and marrow transplantation. The new AWS-based system supports data collection and analysis at scale, using cloud infrastructure and business intelligence tools to improve data quality and data usability across EBMT’s network.

AI is redefining the future. Technology is changing faster than ever; people have new ways of interacting with technology, and organizations are adapting and adopting this change. However, Trusted AI can only be built on trusted data. We will dive deep into how AWS is helping customers build a trusted data foundation as they embark on their AI journey to build outcomes that are tailored to their business needs. HEMA will present their journey towards a strong Data Platform and Data Governance strategy on AWS, and the business outcomes they achieved.

This session explores the building blocks of next-generation data platforms, with a focus on framing the right questions to unlock innovation. We’ll showcase how AWS Glue, ETL pipelines, crawlers, and data catalogs can transform raw data into analytics-ready insights. Drawing on hands-on experience, we’ll share forward-thinking strategies, lessons learned, and emerging best practices to help you architect a data foundation that is intelligent, adaptable, and future-proof.

Join Signify and AWS as we explore the journey of bringing natural language AI control to Philips Hue smart lighting. Follow their path through critical decisions around model selection, evaluation frameworks, and knowledge base architecture as they developed custom classifiers and orchestration flows to transform casual conversation into magical lighting experiences. See how AWS's evolving GenAI capabilities, from foundation model platform to enterprise-grade features, are helping organizations bring innovative AI solutions to production with confidence

Use case of agentic AI applications powered by TiDB and AWS

AWS perspectives on cloud-native generative AI and database requirements

Elliot Foreman and Andrew DeLave from ProsperOps joined Yuliia and Dumky to discuss automated cloud cost optimization through commitment management. As Google go-to-market director and senior FinOps specialist, they explain how their platform manages over $4 billion in cloud spend by automating reserved instances, committed use discounts, and savings plans across AWS, Azure, and Google Cloud. The conversation covers the psychology behind commitment hesitation, break-even point mathematics for cloud discounts, workload volatility optimization, and why they avoid AI in favor of deterministic algorithms for financial decisions. They share insights on managing complex multi-cloud environments, the human vs automation debate in FinOps, and practical strategies for reducing cloud costs while mitigating commitment risks.

Generative AI has incredible potential, but turning that potential into real-world impact requires more than just a proof of concept. Join Matheus Guimaraes for a journey into what it takes to bring generative AI into production — from adoption and development to testing, deployment, and observability. Along the way, you’ll see how new patterns like agentic AI, open-source frameworks like Strands, and AWS services like AgentCore are reshaping the way we build with AI.

Live demos: S3, EC2, Lambda (avec Kiro)

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