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Amazon SageMaker Studio provides a single, web-based visual interface where you can perform all AI model development steps. SageMaker Studio gives you complete access, control, and visibility into each step required to prepare data and build, train, and deploy models. You can quickly upload data, create new notebooks, train and tune models, move back and forth between steps to adjust experiments, compare results, and deploy models to production all in one place, making you much more productive.

Model Context Protocol (MCP) is an open protocol that standardizes how AI models can, in a secure and structured way, access external tools, data sources, and APIs. Join this session to learn best practices on how to securely implement MCP server-based tools to enhance development workflows with richer context in order to help developers complete coding tasks efficiently. We'll also cover relevant use cases and discuss the tradeoffs between using RAG verus MCP.

Amazon EC2 M9g and M9gd instances are now generally available! They are powered by the new AWS Graviton5 processor, the most powerful and most energy efficient processor AWS has ever built. AWS Graviton5 delivers 25% better compute performance compared to Graviton4 for databases, web applications, and agentic AI workloads like real-time reasoning, code generation, and multi-step task orchestration. Learn more about Amazon EC2 M9g Instances: https://go.

AWS provides one place where you can plan work, collaborate on code, and build, test, and deploy applications with continuous integration and continuous delivery (CI/CD) tools. In this session, learn about creating complete CI/CD pipelines using infrastructure as code on AWS. Learn more: More AWS events: https://go.aws/3kss9CP Subscribe: More AWS videos: http://bit.ly/2O3zS75 More AWS events videos: http://bit.

Want to build AWS infrastructure using natural language? Praneeta Prakash and Claudiu Popa join Julian Wood to explore the AWS MCP Server. Discover how to tackle practical challenges like provisioning infrastructure, troubleshooting issues, and orchestrating complex workflows without memorizing API syntax. See how to combine authenticated API access with AWS documentation knowledge through Agent SOPs, natural language workflows that follow AWS best practices.

Learn how financial institutions are transforming fragmented data landscapes into unified AI-powered platforms using Amazon SageMaker Unified Studio. This session shares domain-driven architecture patterns, automated governance frameworks, and enterprise implementation lessons. We’ll dive deep into multi-account domain strategies, data governance, data-as-a-product approaches, and cross-domain consumption patterns.

Learn advanced techniques for building globally distributed, highly available applications using Elastic Load Balancing (ELB). Through demonstrations and real-world use cases, you will learn essential patterns for multi-Availability Zone configurations, multi-Region deployments, and comprehensive disaster recovery solutions. The session covers optimizing global application performance using ELB and Amazon Route 53 to create resilient, globally accessible applications.

Is your AMI or EBS snapshot creation slow or stuck in the Pending state? Neshon explains why it happens (full vs. incremental snapshots, multiple volumes, stacked snapshots) and shows how to create snapshots and automate them with Data Lifecycle Manager. 0:00 Introduction 0:21 Full vs.

Foundation models like NVIDIA GR00T provide powerful starting points for humanoid robotics—but customization is essential for real-world applications. Learn how to fine-tune GR00T and other robotics foundation models at scale using AWS Batch and Amazon SageMaker.

Amazon Inspector offers continuous monitoring of your Amazon EC2 instances for software vulnerabilities without installing an agent or additional software. With this capability, you can expand your vulnerability assessment coverage across your EC2 infrastructure with Amazon Inspector agentless scanning for EC2 instances that do not have SSM Agents installed or configured.

Amazon Redshift RG instances, powered by AWS Graviton, deliver better performance - running data warehouse and data lake workloads up to 2.4x as fast as RA3 - at 30% lower price per-vCPU. Run SQL analytics across your data warehouse and data lake from a single engine — including on open formats such as Apache Iceberg and Apache Parquet.

Everyone talks about data governance — but few can explain what it actually means in practice. In this episode, we break it down to its essentials: understanding, curating, and protecting data so it supports real business outcomes. You’ll learn why data governance isn’t about control or bureaucracy, but about enabling trust, quality, and business value. We’ll also share how to get started and what not to measure when building your program. Learn more about Amazon SageMaker Catalog - http://go.