Tag: Internet

Amazon Managed Service for Prometheus collector, a fully-managed agentless collector for Prometheus metrics now integrates with the Amazon EKS access management controls. Starting today, the collector utilizes the EKS access management controls to create a managed access policy that allows the collector to discover and collect Prometheus metrics. Amazon Managed Service for Prometheus collector with support for EKS access management controls is available in all regions where Amazon Managed Service for Prometheus is available. To learn more about Amazon Managed Service for Prometheus collector, visit the user guide or product page.

Amazon Relational Database Service (RDS) for PostgreSQL now supports pgvector 0.7.0, an open-source extension for PostgreSQL for storing vector embeddings in your database, letting you use retrieval-augemented generation (RAG) when building your generative AI applications. This release of pgvector includes features that increase the number of dimensions of vectors you can index, reduce index size, and includes additional support for using CPU SIMD in distance computations. pgvector 0.7.0 adds two new vector data types: halfvec for storing dimensions as 2-byte floats, and sparsevec for storing up to 1,000 nonzero dimensions, and now supports indexing binary vectors using the PostgreSQL-native bit type. These additions let you use scalar and binary quantization for the vector data type using PostgreSQL expression indexes, which reduces the storage size of the index and lowers the index build time. Quantization lets you increase the maximum dimensions of vectors you can index: 4,000 for h

We are pleased to announce general availability of Amazon EC2 G6 instances on SageMaker notebooks. Amazon EC2 G6 instances are powered by up to 8 NVIDIA L4 Tensor Core GPUs with 24 GB of memory per GPU and third generation AMD EPYC processors. G6 instances offer 2x better performance for deep learning inference compared to EC2 G4dn instances. Customers can use G6 instances to interactively test model deployment and for interactive model training for use cases such as generative AI fine-tuning and inference workloads, natural language processing, language translation, computer vision, and recommender engines. Amazon EC2 G6 instances are available for SageMaker notebooks in the AWS US East (N. Virginia and Ohio) and US West (Oregon) regions. Visit developer guides for instructions on setting up and using JupyterLab and CodeEditor applications on SageMaker Studio and SageMaker notebook instances.

Amazon Cognito introduces pricing for machine-to-machine (M2M) authentication to better support continued growth and expand capabilities. There is no change to Amazon Cognito's user based pricing (monthly active users or MAUs). Customer accounts currently using Amazon Cognito for M2M use cases will be exempt from pricing for 12 months. M2M pricing is based on the number of application clients configured for M2M authentication and the number of tokens requested for them. You can find details on our pricing page. Amazon Cognito makes it easier to add authentication, authorization, and identity management to your web and mobile apps. In addition to supporting human identities, Cognito's M2M authentication enables developers to leverage machine identities to secure interactions between their services or across organizations. Developers can define machine identities and generate OAuth 2.0 tokens to authenticate them using Cognito user pools that are configured with the OAuth 2.0 client

Amazon MQ now provides support for RabbitMQ version 3.12.13, which includes several fixes and performance improvements to the previous versions of RabbitMQ supported by Amazon MQ. Starting from RabbitMQ 3.12.13, all Classic Queues on Amazon MQ brokers are upgraded to Classic Queues version 2 (CQv2) automatically. All queues on RabbitMQ 3.12 now behave similarly to lazy queues. These changes provide a significant improvement to throughput and lower memory usage for most use cases.  If you are running earlier versions of RabbitMQ, such as 3.8, 3.9, 3.10 or 3.11, we strongly encourage you to upgrade to RabbitMQ 3.12.13. This can be accomplished with just a few clicks in the AWS Management Console. We also encourage you to enable automatic minor version upgrades on RabbitMQ 3.12.13 to help ensure your brokers take advantage of future fixes and improvements.  Amazon MQ for RabbitMQ will end support for RabbitMQ versions 3.8, 3.9 and 3.10 as indicated in the version

Amazon Relational Database Service (RDS) for PostgreSQL now supports the latest minor versions PostgreSQL 16.3, 15.7, 14.12, 13.15, and 12.19. This release of RDS for PostgreSQL also includes support for pgvector 0.7.0, which lets you index vectors larger than 2,000 dimensions and adds support for scalar and binary quantization through expression indexes.  The PostgreSQL community released PostgreSQL 16.3 minor version as of today. We recommend that you upgrade to the latest minor versions to fix known security vulnerabilities in prior versions of PostgreSQL, and to benefit from the bug fixes added by the PostgreSQL community. You are able to leverage automatic minor version upgrades to automatically upgrade your databases to more recent minor versions during scheduled maintenance window. Learn more about upgrading your database instances in the Amazon RDS User Guide. Amazon RDS for PostgreSQL makes it simple to set up, operate, and scale PostgreSQL deployments in the cloud. See A

Amazon Elastic Container Registry (ECR) now includes GitLab Container Registry as a supported upstream registry for ECR’s pull through cache feature. With today’s release, customers using GitLab’s software-as-a-subscription offering, GitLab.com, can automatically sync images from the newly supported upstream registry to their private ECR repositories. ECR customers can create a pull through cache rule that maps an upstream registry to a namespace in their private ECR registry. Using Amazon ECR Pull through cache support with GitLab Container Registry requires authentication. Customers can provide credentials that are stored in AWS Secrets Manager and are used to authenticate to the upstream registry. Once rule is configured, images can be pulled through ECR from GitLab Container Registry. ECR automatically creates new repositories for cached images and keeps them in-sync with the upstream registry. Additionally, customers can use repository creation templates (in preview) to spec

Amazon QuickSight is excited to announce the launch of SPICE capacity auto-purchase API. Previously, customers were required to manually turn on SPICE auto-purchase via the console UI. Now with this API enhancement, QuickSight users can programmatically turn on the SPICE capacity auto-purchase, seamlessly integrating it into their adoption and migration pipeline. Once turned on, users don’t need to estimate SPICE usage and manually purchase capacity each time. Instead, they can seamlessly ingest data and use SPICE worry free, as QuickSight will automatically acquire the necessary capacity to meet their usage requirements. For further details, visit here. The new SPICE capacity auto-purchase API is now available in Amazon QuickSight Enterprise Editions in all QuickSight regions - US East (N. Virginia and Ohio), US West (Oregon), Canada, Sao Paulo, Europe (Frankfurt, Stockholm, Paris, Ireland and London), Asia Pacific (Mumbai, Seoul, Singapore, Sydney and Tokyo), and the AWS GovCloud (

Amazon Connect now provides enhanced search capabilities for flows and flow modules on the Connect admin website and programmatically using APIs. You can now search for flows and flow modules by name, description, type, status, and tags, making it easy to filter and identify a specific flow when managing your Connect instances. For example, you can now search for all flows tagged with the Department:Help_Desk key value pair to filter your set of flows down to the specific ones you are looking for. This feature is supported in all AWS regions where Amazon Connect is offered. To learn more about Connect Flows and AWS see the Amazon Connect Administrator Guide and Amazon Connect API Reference. To learn more about Amazon Connect, the AWS cloud-based contact center, please visit the Amazon Connect website.

Amazon SageMaker now integrates with Amazon DataZone making it easier for customers to access machine learning (ML) infrastructure, data and ML assets. This integration will unify data governance across data and ML workflows. ML administrators can setup the infrastructure controls and permissions for ML projects in Amazon DataZone. Project members can collaborate on business use cases and share assets with one another. Data scientists and ML engineers can then create a SageMaker environment and kick start their development process inside SageMaker Studio. Data scientists and ML engineers can also search, discover, and subscribe to data and ML assets in their business catalog within SageMaker Studio. They can consume these assets for ML tasks such as data preparation, model training, and feature engineering in SageMaker Studio and SageMaker Canvas. Upon completing the ML tasks, data scientists and ML engineers can publish data, models, and feature groups to the business catalog for gove