AWS News Blog
Author: Antje Barth
Antje Barth is a Principal Developer Advocate for generative AI at AWS. She is co-author of the O’Reilly books Generative AI on AWS and Data Science on AWS. Antje frequently speaks at AI/ML conferences, events, and meetups around the world. She also co-founded the Düsseldorf chapter of Women in Big Data.
Knowledge Bases for Amazon Bedrock now supports Amazon Aurora PostgreSQL and Cohere embedding models
During AWS re:Invent 2023, we announced the general availability of Knowledge Bases for Amazon Bedrock. With a knowledge base, you can securely connect foundation models (FMs) in Amazon Bedrock to your company data for Retrieval Augmented Generation (RAG). In my previous post, I described how Knowledge Bases for Amazon Bedrock manages the end-to-end RAG workflow […]
AWS Weekly Roundup — Amazon ECS, RDS for MySQL, EMR Studio, AWS Community, and more — January 22, 2024
As usual, a lot has happened in the Amazon Web Services (AWS) universe this past week. I’m also excited about all the AWS Community events and initiatives that are happening around the world. Let’s take a look together! Last week’s launches Here are some launches that got my attention: Amazon Elastic Container Service (Amazon ECS) […]
Amazon SageMaker Studio adds web-based interface, Code Editor, flexible workspaces, and streamlines user onboarding
Today, we are announcing an improved Amazon SageMaker Studio experience! The new SageMaker Studio web-based interface loads faster and provides consistent access to your preferred integrated development environment (IDE) and SageMaker resources and tooling, irrespective of your IDE choice. In addition to JupyterLab and RStudio, SageMaker Studio now includes a fully managed Code Editor based […]
Package and deploy models faster with new tools and guided workflows in Amazon SageMaker
I’m happy to share that Amazon SageMaker now comes with an improved model deployment experience to help you deploy traditional machine learning (ML) models and foundation models (FMs) faster. As a data scientist or ML practitioner, you can now use the new ModelBuilder class in the SageMaker Python SDK to package models, perform local inference […]
Amazon SageMaker adds new inference capabilities to help reduce foundation model deployment costs and latency
Today, we are announcing new Amazon SageMaker inference capabilities that can help you optimize deployment costs and reduce latency. With the new inference capabilities, you can deploy one or more foundation models (FMs) on the same SageMaker endpoint and control how many accelerators and how much memory is reserved for each FM. This helps to […]
Amazon SageMaker Clarify makes it easier to evaluate and select foundation models (preview)
I’m happy to share that Amazon SageMaker Clarify now supports foundation model (FM) evaluation (preview). As a data scientist or machine learning (ML) engineer, you can now use SageMaker Clarify to evaluate, compare, and select FMs in minutes based on metrics such as accuracy, robustness, creativity, factual knowledge, bias, and toxicity. This new capability adds […]
Evaluate, compare, and select the best foundation models for your use case in Amazon Bedrock (preview)
I’m happy to share that you can now evaluate, compare, and select the best foundation models (FMs) for your use case in Amazon Bedrock. Model Evaluation on Amazon Bedrock is available today in preview. Amazon Bedrock offers a choice of automatic evaluation and human evaluation. You can use automatic evaluation with predefined metrics such as […]
Introducing Amazon SageMaker HyperPod, a purpose-built infrastructure for distributed training at scale
Today, we are introducing Amazon SageMaker HyperPod, which helps reducing time to train foundation models (FMs) by providing a purpose-built infrastructure for distributed training at scale. You can now use SageMaker HyperPod to train FMs for weeks or even months while SageMaker actively monitors the cluster health and provides automated node and job resiliency by […]