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Santosh shows you how to use an Amazon OpenSearch serverless collection with a private network policy to create a knowledge base in Amazon Bedrock. For more details on this topic, visit the AWS Knowledge Center on AWS re:Post and read the full article associated with this video: https://repost.

​This video presents AWS's "Model as a Service" (MoDaaS) solution, built on the TM Forum ODA canvas in partnership with Ericsson, Databricks, and MuleSoft, designed to help telecom operators move agentic AI solutions from proof-of-concept to production. The architecture implements a seven-layer trust chain (declare, admit, approve, authorize, enforce, attest, and revoke) ensuring that AI agents can only access approved models, tools, and governance policies.

In this episode, you'll explore the operational side of GenAI: cost optimization, troubleshooting approaches, and monitoring best practices. We'll work through practice exam questions that test your knowledge and wrap up with final exam tips and insights from real-world operational experiences to help you prepare for certification success. Resource links: - Monitoring the performance of Amazon Bedrock: https://docs.aws.amazon.com/bedrock/latest/userguide/monitoring.

EC2 Mac instances allow you to run on-demand macOS workloads in the cloud and extend the flexibility, scalability, and cost benefits of AWS to all Apple developers. In this session, you will learn about Apple application development in the cloud using EC2 Mac instances. We will share the latest features and advancements for EC2 Mac and how customers are benefiting from them.

Learn how to setup Amazon SageMaker Studio to accelerate AI model development and steamline collaboration on data science projects. This demo will cover dmoan setup, security features and customization. Subscribe to AWS: https://go.aws/subscribe Create a free AWS account: https://go.aws/signup Try AWS for free: https://go.aws/free Connect with an expert: https://go.aws/contact Explore more: https://go.aws/more Next steps: Explore on AWS in Analyst Research: https://go.

As organizations modernize applications for scalability and agility, database architecture becomes crucial. This session explores Amazon Aurora DSQL, a serverless, distributed SQL database for next-generation workloads. Learn to build modern serverless applications using Amazon Aurora DSQL with Amazon API Gateway and AWS Lambda. Examine performance characteristics, scaling behavior, and cost considerations, along with current limitations.

This session introduces Baby Dragon Hatchling [BDH], a post-Transformer architecture designed to solve the core barrier faced by today’s AI: its inability to generalize over time. Join to learn how BDH, based on a scale-free biologically inspired network, has overcome the static nature of traditional LLM models. In this talk, we will showcase how next-generation AI systems built on BDH can adapt continuously from experience while providing inherent interpretability and compute efficiency.

Learn how to go from finding data in Amazon SageMaker Catalog to building shareable dashboards in Amazon Quick Suite in a single, secure workflow. This session shows how the integration between the next generation of Amazon SageMaker and Amazon Quick Suite brings business intelligence dashboard governance into the same environment where you discover and prepare data. See how to manage access, ensure compliance, and share trusted insights across teams without moving data between systems.

Discover how Amazon SageMaker Unified Studio surfaces data quality metrics from multiple tools including AWS Glue Data Quality in one unified view, enabling data consumers to gain trust in datasets before subscribing. In this demo, learn how to view overall quality scores with individual rule results, examine historical data quality trends to detect drift over time, and understand which specific data issues like missing values, duplicates, or stale data need attention.

Learn how to deploy AI agents with controls that restrict what they can say and do. This session covers Amazon Bedrock Guardrails that filter agent content (input/output) and Amazon Bedrock AgentCore Policy that enforces rules on agent actions. Attendees will learn how to: - Choose between guardrails (controlling input/output) and policy (controlling actions) - Define policies that enforce rules on agent actions - How AWS uses automated reasoning to validate controls.

Discover how AI agents are revolutionizing enterprise-scale code modernization initiatives. This session demonstrates how to accelerate your modernization journey through agentic AI-driven code transformations. You'll learn practical strategies for defining custom code transformations, setting up transformation campaigns, managing multi-repository migrations, and measuring success.

Orchestrate multi-agent systems that work across AI platforms and automate complex workflows. This presentation is brought to you by IBM, an AWS Partner. Learn more: More AWS events: https://go.aws/3kss9CP Subscribe: More AWS videos: http://bit.ly/2O3zS75 More AWS events videos: http://bit.ly/316g9t4 ABOUT AWS: Amazon Web Services (AWS) hosts events, both online and in-person, bringing the cloud computing community together to connect, collaborate, and learn from AWS experts.