Architecture Best Practices for Machine Learning
AWS re:Invent 2025
CEO Matt Garman shares how AWS is shaping the future of cloud tech
Join AWS CEO Matt Garman to hear how AWS is innovating across every aspect of the world’s leading cloud. He explores how we are reinventing foundational building blocks as well as developing brand new experiences, all to empower customers and partners with what they need to build a better future.
AI agents in action: Architecting the future of applications
Explore how agentic AI is transforming cloud-native application architecture, unlocking faster innovation cycles and entirely new application patterns. Learn how new AWS capabilities empower builders to design secure, reasoning-driven agents that orchestrate data, code, and tools at scale, with an emphasis on governance, reliability, and cost efficiency. Discover how AWS customers are deploying production-ready agents today, and learn best practices to help you architect agentic applications that autonomously adapt, optimize, and act in real-time.
AI model benchmarking with Amazon SageMaker, Amazon Bedrock & AWS IoT Greengrass
Deploying AI models to edge devices poses unique challenges in balancing hardware requirements, power consumption, and model performance. Through practical examples using Amazon SageMaker distributed training and AWS IoT Greengrass, discover approaches for automated model deployment, validation, and performance monitoring across edge devices. Learn how Strands Agents and Amazon Bedrock can supplement lightweight on-device models with large foundational models. Explore techniques for aggregating results in a Jupyter-based dashboard for rapid prototyping and optimization, and coordinating edge device models with Amazon Bedrock foundational models for aggregating data and in-depth analysis. This session provides strategies for architecting scalable AI pipelines optimized for edge deployments.
Building Pipelines for Analytics, ML and AI in Amazon Sagemaker Unified Studio
Learn how to create end-to-end pipelines to power data and AI applications in Amazon SageMaker Unified Studio. We will cover how to implement both batch and streaming pipelines to integrate various data sources, optimizing data movement with modern ETL techniques. This session will equip you with the knowledge to develop comprehensive data and AI solutions using the next generation of Amazon SageMaker, from initial data processing through to model deployment.