Best Summit AWS notifications in New York, 2025 | Amazon Web Services

Today at the AWS Summit in New York, SIVAMI provided Sivasubramanian, VP agents AI AI, a daily main comment on how to supply customers to AI agents ready to make a scale. Here is our rounding of the biggest announcement of the event:

Introducing Amazon Bedrock Agentcore: Safely deploy and run AI agents on any scale (preview)
Amazon Bedrock Agentcore enables fast deployment and scaling of AI agents with business security. It provides memory management, identity control and integration-breaking tools when working with any open source frame and endowment model.

AMAZON NOVA Customization Notification in Amazon Sagemaker AI
The AWS now allows extensive adaptation of Amazon Nova Foundation models via Sagemaker AI at all stages of model training. These skills, which are available as recipes for use, allow customers to adapt models of Nova understanding in pre -school and after training, including recipes for fine -tuning and alignment to better deal with the requirements of business specific.

AWS AI League: Learn, innovate and compete in our new Ultimate AI Showdown
AWS AI League is a program that helps organizations to increase their workforce by combining entertainment competition with practical learning using AIS AI Services. It offers a unique opportunity for businesses and developers to acquire valuable and practical skills in fine fine-tuning, adapting the model and fast engineering-basic skills for building generative solutions AI.

AWS FREE: New customers can start and explore AWS with credits up to $ 200
The AWS improves its free levels program with up to $ 200 for new users: $ 100 for registration and another $ 100, obtained by completing activities with services such as Amazon EC2, Amazon Bedrock and AWS budgets.

Tweelvelabs video understanding models are now available in Amazon Bedrock
Tweelvelabs video understanding models are now available on Amazon Bedrock and allow customers to search videos, classify scenes, summarize content and extract knowledge with accuracy and reliability.

Amazon S3 metadata now supports metadata for all your S3 objects
The Amazon S3 metadata now provides comprehensive visibility of all S3 objects through live tables inventory and diary, allowing both existing and new SQLs to be analyzed with automatic updates within one hour of changes.

Introducing Amazon S3 vectors: First cloud storage with native vector support in scale (preview)
The Amazon S3 Vectors is a new cloud object store that provides native support for storing and interviewing vectors on a massive scale and offers up to 90% reduction in costs compared to conventional approaches, while it integrates with knowledge foundations Amazon Bedrock, SageMaker and OpenSearch for AI.

Simplify the way from data to knowledge with new Amazon Sagemaker abilities
Amazon Sagemaker introduced three new abilities – Amazon QuickSight Integration for creating dashboard, management and sharing, Amazon S3 Unstructured data integration for cataloging and media files, and automatic deck data from Lakehouse – that eliminates data siles, silverness, visualization and management and governance.

Monitor and tune in applications controlled with new Amazon Eventbridge logging
Amazon EventBridge now offers improved logging options that provide comprehensive life cycle monitoring, help users track and eliminate problems with their applications based on detailed protocols showing when events are published, corresponding to rules, delivered to subscribers or meeting failures.

Accelerate Safe Software Relaxing New Built -in Blue/Green deployment in Amazon ECS
Make more secure container applications without tools to deploy your own deployment, allowing you to send software updates more often with an almost unforeseen return capability.

Amazon EKS allows ultra -scales of AI/ml workload with support for 100k nodes per cluster
Amazon EKS now changes to 100,000 nodes per cluster, allowing massive workload AI/ml with up to 1.6 m AWS Accelerators or 800K NVIDIA GPUs. This allows organizations to effectively train and run large AI models while maintaining Kubernetes compatibility and existing tool integration.

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