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.
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 […]
Amazon Titan Image Generator, Multimodal Embeddings, and Text models are now available in Amazon Bedrock
Today, we’re introducing two new Amazon Titan multimodal foundation models (FMs): Amazon Titan Image Generator (preview) and Amazon Titan Multimodal Embeddings. I’m also happy to share that Amazon Titan Text Lite and Amazon Titan Text Express are now generally available in Amazon Bedrock. You can now choose from three available Amazon Titan Text FMs, including […]
Introducing Amazon Q, a new generative AI-powered assistant (preview)
Update April 30, 2024: Amazon Q Business now generally available! Today, we are announcing Amazon Q, a new generative artificial intelligence- (AI)-powered assistant designed for work that can be tailored to your business. You can use Amazon Q to have conversations, solve problems, generate content, gain insights, and take action by connecting to your company’s […]
Guardrails for Amazon Bedrock helps implement safeguards customized to your use cases and responsible AI policies (preview)
As part of your responsible artificial intelligence (AI) strategy, you can now use Guardrails for Amazon Bedrock (preview) to promote safe interactions between users and your generative AI applications by implementing safeguards customized to your use cases and responsible AI policies. AWS is committed to developing generative AI in a responsible, people-centric way by focusing […]