12/9/2022, 12:00:00 AM ~ 12/12/2022, 12:00:00 AM (UTC)

Recent Announcements

Amazon ECS Service Connect now supports AWS Fargate on AWS Graviton Processors

Amazon Elastic Container Service (Amazon ECS) Service Connect now supports AWS Fargate workloads running on AWS Graviton Processors. Service Connect simplifies service discovery, connectivity and traffic observability for Amazon ECS and helps you build applications faster by letting you focus on the application code instead of your networking infrastructure.

Amazon Managed Grafana adds support for AWS CloudFormation

Amazon Managed Grafana now supports AWS CloudFormation. You can use AWS CloudFormation templates to create, update, and delete your Amazon Managed Grafana workspaces, as well as manage or update workspace SAML authentication settings.

Announcing Global Free Tier Pricing Rules for AWS Billing Conductor

Starting today, customers can now disable Always Free Tier offers when creating a global pricing rule in AWS Billing Conductor (ABC). Pricing rules with free tier disabled allow customers to default to the first paid tier for all AWS services. For example, AWS partners can use a global pricing rule with free tier disabled to match the negotiated terms between themselves and their end customers. This feature makes it easier to align the Billing Conductor’s pro forma billing reports with internal chargeback logic.

Announcing preview of SageMaker Model Training support for ml.p4de instances

Amazon SageMaker model training now supports preview of ml.p4de.24xlarge(p4de) instances, powered by NVIDIA A100 GPUs and high-performance HBM2e GPU memory. These instances provide the highest performing compute infrastructure currently available for use cases that require training of large language models (LLMs), computer vision (object detection, semantic segmentation) and generative artificial intelligence.

Amazon Braket now supports adjoint gradient computation, unlocking runtime improvements and cost savings

SV1, the on-demand state vector simulator on Amazon Braket, now supports the computation of gradients using the adjoint differentiation method, enabling customers to reduce runtime and save costs for their quantum machine learning and optimization workloads. With this launch, customers simulating variational quantum algorithms with a large number of parameters, such as the quantum approximate optimization algorithm (QAOA), can now seamlessly incorporate adjoint gradient computation either directly from the Braket Python SDK or API, or through PennyLane, an open-source software framework built for quantum differentiable programming.

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