5/10/2024, 12:00:00 AM ~ 5/13/2024, 12:00:00 AM (UTC)

Recent Announcements

Amazon Managed Service for Prometheus collector integrates with Amazon EKS access management controls

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 RDS for PostgreSQL supports pgvector 0.7.0

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 halfvec and 64,000 for binary vectors. pgvector 0.7.0 also adds functions to calculate both Hamming and Jaccard distance for binary vectors. pgvector 0.7.0 is available on database instances in Amazon RDS running PostgreSQL 16.3 and higher, 15.7 and higher, 14.12 and higher, 13.15 and higher, and 12.19 and higher in all applicable AWS Regions, including the AWS GovCloud (US) Regions. Amazon RDS for PostgreSQL makes it simple to set up, operate, and scale PostgreSQL deployments in the cloud. See Amazon RDS for PostgreSQL Pricing for pricing details and regional availability. Create or update a fully managed Amazon RDS database in the Amazon RDS Management Console.

Amazon SageMaker notebooks now support G6 instance types

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.

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