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Learn about using Amazon Bedrock to optimize for cost, latency, and accuracy for your generative AI workloads. Learn more - https://go.aws/3JoiVnK Subscribe to AWS: https://go.aws/subscribe Sign up for AWS: https://go.aws/signup AWS free tier: https://go.aws/free Explore more: https://go.aws/more Contact AWS: https://go.aws/contact Next steps: Explore on AWS in Analyst Research: https://go.aws/reports Discover, deploy, and manage software that runs on AWS: https://go.

Software developers face challenges that go beyond coding, such as preparing design documentation, deep diving into architecture component choices, making complex decisions, and expediting ticket resolution. With Quick, developers can dramatically speed up decision-making by eliminating the need to manually navigate scattered documentation, tribal knowledge, and switching between tools. For instance, they may want to understand security policies for an application they’re building.

Discover how to architect scalable, production-ready agentic AI applications on AWS that deliver autonomous, multi-agent solutions for complex business challenges. Many organizations struggle with designing AI agents for real-world complexity, from managing context effectively to ensuring secure, scalable deployments.

Linedata, a firm serving the financial services community, collaborated with AWS Gen AI Innovation Center to transform investment compliance processes using generative AI. To address the manual, time-consuming nature of compliance processes in asset management firms, Linedata leveraged AWS generative AI capabilities to automate and streamline compliance workflows.

AWS and Cerebras announced a collaboration to set a new standard for AI inference speed and performance in the Cloud, which will be available through Amazon Bedrock. The solution combines AWS Trainium3-powered servers, Cerebras CS-3 systems, and Elastic Fabric Adapter (EFA) networking. The Trainium3 + CS-3 solution enables “inference disaggregation,” a technique which separates AI inference into two stages: prompt processing, or “prefill,” and output generation, or “decode.

In this comprehensive tutorial, Ashita and Varsha demonstrate how to transform a basic CRUD application into a production-ready, scalable hackathon idea submission platform using AWS serverless technologies. This hands-on guide covers the complete journey from localhost development to globally distributed deployment, showing developers how to build cloud-native applications that can handle real-world traffic patterns and scale automatically from zero to thousands of users.

As organizations build and customize multiple AI models and agents, managing experiments, versions, and evaluations becomes increasingly complex. In this session, see how Intuit, a leader in financial technology, is using Amazon SageMaker with MLflow to accelerate their AI initiatives and empower their teams to build and deploy AI solutions with confidence.

Amazon and AWS have evolved beyond AI-assisted development to embrace AI Native practices, integrating AI as a partner throughout the software development lifecycle. Learn how their teams leverage AWS foundational tools including Amazon Q Developer, Kiro, and Amazon Bedrock. Discover effective Prompt Driven Development methodologies and grassroots adoption strategies from early champions.

Organizations are rapidly building AI applications and workflows to automate manual processes and boost productivity. The reality: many of these initiatives never reach production. This session explores the critical challenges of building, deploying, and scaling end-to-end enterprise AI systems and shares proven technical architectures and best practices to go from POC to production with Amazon Bedrock.

Explore the future of enterprise security with Abnormal AI's CIO Mike Britton, as he reveals how next-generation security operations are evolving to combat machine-speed threats. As both a security leader and AI innovator, Britton shares his advice for implementing effective agentic AI governance while maintaining operational agility. He emphasizes that success in the AI era isn't about replacing humans, but about empowering security teams to work alongside AI systems effectively.

Amazon Simple Storage Service turned 20 on March 14th, 2026. Discover the incredible 20-year journey of Amazon S3, the first building block of AWS. Learn how this service has shaped cloud computing and empowered developers worldwide. Check out the full video → https://go.aws/4sVKXYS #AWS #AmazonS3 #CloudStorage

Security teams face alert overload and compliance complexity. This code talk shows how to build AI-driven automation that transforms security operations. Through live coding demonstrations, we'll develop intelligent agents that process security events, analyze threats, and orchestrate automated responses. You'll learn to implement real-time detection systems, incident management workflows, and smart remediation patterns.