The attendee’s guide to the AWS re:Invent 2026 AI/ML track

From November 30th to December 4th, Amazon Web Services (AWS) will hold its annual premier learning event: re:Invent . At this event, attendees will become stronger and more proficient in various areas of AWS technology through a variety of experiences: keynotes given by AWS leaders, interactive working sessions led by AWS experts, and fun, immersive activities such as live entertainment and games at re:Play . There are over 2,000 learning sessions that focus on specific topics at various skill levels, and the AI/ML (AIM) track has more than 140 unique sessions for you to choose from. We’re here to help you identify the sessions that best fit your needs. Even if you can’t join in person, you can catch up with many of the sessions on demand and even watch the keynotes live. The basics: Session types If you can join us, then remember that we offer several types of sessions that can help maximize your learning in a variety of AWS topics. re:Invent attendees can choose to attend breakouts, chalk talks, builder sessions, workshops, and code talks. The breakout sessions are recorded and uploaded a few days after to the AWS Events YouTube channel , while the rest are live non-recorded interactive sessions. Here are the details for each session type: Breakout sessions: Attendees are in a lecture-style 60-minute informative session presented by AWS experts, customers, and partners. Chalk-talk sessions: Attendees interact with presenters, asking questions, and using a whiteboard in session. Builder sessions: Attendees participate in a one-hour session and build something. Workshop sessions: Attendees join a two-hour interactive session where you work in a small team to solve a real problem using AWS services. Code talk sessions: Attendees participate in engaging code-focused sessions where an expert leads a live coding session. Lightning talk sessions: Attendees watch a 20-minute demo dedicated to either a specific service or customer story (located in the Venetian Expo Hall or Wynn/Encore Upper Convention Promenade). With so many sessions to pick from, it helps to know how the pieces fit together. Here is how AWS thinks about its AI offerings, so you can decide where to begin and where to focus. The AWS AI stack overview and tie-in to re:Invent sessions AWS offers a comprehensive AI portfolio in the cloud. It covers everything from the physical infrastructure and AI chips that power AI workloads up through ready-to-use agentic solutions that teams can adopt today. Understanding how these services work together helps you navigate re:Invent and focus on the sessions most relevant to what you’re building. Figure 1: The AWS AI stack Global Infrastructure – It all starts with data centers designed and optimized for AI workloads. These facilities are built to handle the power, cooling, and networking demands that large AI training and inference jobs require. AI Chips – Running inside those data centers are two compute options: AWS Trainium , purpose-built for high-performance AI training and inference, and NVIDIA GPUs for teams that need GPU-based acceleration. Customers choose based on their workload, performance requirements, and cost targets. Data Foundation – AI is only as good as the data behind it. AWS Context connects your data across storage, databases, analytics, and search services, giving models and agents access to the right information at the right time. Models, Training, and Inferencing – This is where models get built, trained, fine-tuned, and served. Amazon Bedrock gives you managed access to hundreds of leading models from multiple providers through a unified API. Amazon SageMaker AI gives you full control to train, fine-tune, and deploy custom models on dedicated infrastructure. Many customers use both depending on the workload. Agent Development and Management – Amazon Bedrock AgentCore is where you build, deploy, and manage AI agents. It supports any model or provider mix, including models from both Amazon Bedrock and SageMaker AI, so teams can design agents that pick the right model for each task without being locked into a single provider. Agentic Solutions – At the top of the stack sit purpose-built agents that are ready to use. Kiro helps developers ship correctly the first time with spec-driven development. AWS Quick connects your applications, tools, and data into a personal knowledge graph that learns how you work. Amazon Connect delivers agents for customer experience, hiring, supply chain, and healthcare. AWS Marketplace gives you access to partner-built AI solutions. Security, Governance, and Cost Optimization run across every layer of the AI stack. They are built into the foundation, with hardware-level isolation at the chip layer and controls that enforce what agents can access and do. The AI-focused sessions at re:Invent span the full stack. Whether you’re evaluating AI chips, serving models in production, building custom models, deploying agents, or adopting ready-to-use solutions, this guide helps you find the sessions that matter most. Use it to build a smarter re:Invent schedule. The artificial intelligence and machine learning (AI/ML) track primarily focuses on three core layers of this stack: AI chips , models, training, and inference , and agent development and management , with security, governance, and cost optimization woven throughout. The following sections start at the chip layer and move up through models and agents. Then come sessions that tie the stack together. The guide ends with other relevant topics, such as sustainability and infrastructure where you run your AI workloads. Within each section, sessions for newcomers come first and technical deep dives come last. Want to skip ahead? Jump straight to the topic you care about: AWS AI platform sessions AI chips: NVIDIA GPUs and AWS Trainium Amazon SageMaker AI sessions Build and run AI applications and agents with Amazon Bedrock Build AI that works with documents, voice, and images Building trust in AI agents Other topics Plan your re:Invent AWS AI platform sessions These sessions cover the core AWS AI services together: Amazon Bedrock, Amazon SageMaker AI, Amazon Bedrock AgentCore, and the AI chips that they run on. They help you make architecture choices that still work as your usage grows. New to AI on AWS? Start with AIM204 . It walks through the whole stack. You will see Trainium and Graviton chips, model inference on Amazon Bedrock and SageMaker AI, and production agents on AgentCore. If you’re already building, try AIM364 . It helps you choose between Amazon Bedrock and SageMaker AI, and shows when to use both. The example is a real agentic coding app that runs on AgentCore. Other sessions dig into specific production problems. If you own data strategy, attend AIM363 . This chalk talk shows how governed data products feed customization, inference, and agents. It also explains why cost levers like prompt caching, distillation, and batch inference work better on clean data. Need to prove the value of AI? AIM319 looks past cost to business impact. A live demo across the AI stack shows how to measure return on investment (ROI) and compare cost per outcome. Experienced builders should look at AIM443 . In this 400-level chalk talk, you build a training pipeline on Amazon SageMaker HyperPod. You bring in company data and keep governance in place. Then you deploy the same model to Amazon Bedrock for inference or to AgentCore for agents. AI chips: NVIDIA GPUs and AWS Trainium AWS gives you two main choices of AI chips for training and inference: NVIDIA GPUs and AWS Trainium. Start with the one you use today, then explore the other. NVIDIA GPU sessions AWS offers a wide range of Amazon Elastic Compute Cloud (Amazon EC2) instances with NVIDIA GPUs. These sessions show how to run AI training and inference on them securely and reliably. Start with AIM2210 for the latest on GPU instances. In AIM3301 , a customer shares how it gets the most from its GPU capacity by optimi