Category: Announcements

Large language model (LLM) training has become increasingly popular over the last year with the release of several publicly available models such as Llama2, Falcon, and StarCoder. Customers are now training LLMs of unprecedented size ranging from 1 billion to over 175 billion parameters. Training these LLMs requires significant compute resources and time as hundreds […]

Launched in 2019, Amazon SageMaker Studio provides one place for all end-to-end machine learning (ML) workflows, from data preparation, building and experimentation, training, hosting, and monitoring. As we continue to innovate to increase data science productivity, we’re excited to announce the improved SageMaker Studio experience, which allows users to select the managed Integrated Development Environment (IDE) […]

As organizations scale the adoption of machine learning (ML), they are looking for efficient and reliable ways to deploy new infrastructure and onboard teams to ML environments. One of the challenges is setting up authentication and fine-grained permissions for users based on their roles and activities. For example, MLOps engineers typically perform model deployment activities, […]

Today, we are excited to announce support for Code Editor, a new integrated development environment (IDE) option in Amazon SageMaker Studio. Code Editor is based on Code-OSS, Visual Studio Code Open Source, and provides access to the familiar environment and tools of the popular IDE that machine learning (ML) developers know and love, fully integrated […]

In the last few years Large Language Models (LLMs) have risen to prominence as outstanding tools capable of understanding, generating and manipulating text with unprecedented proficiency. Their potential applications span from conversational agents to content generation and information retrieval, holding the promise of revolutionizing all industries. However, harnessing this potential while ensuring the responsible and […]

The rapid growth of generative AI brings promising new innovation, and at the same time raises new challenges. These challenges include some that were common before generative AI, such as bias and explainability, and new ones unique to foundation models (FMs), including hallucination and toxicity. At AWS, we are committed to developing generative AI responsibly, […]

Amazon Elastic Compute Cloud (Amazon EC2) accelerated computing portfolio offers the broadest choice of accelerators to power your artificial intelligence (AI), machine learning (ML), graphics, and high performance computing (HPC) workloads. We are excited to announce the expansion of this portfolio with three new instances featuring the latest NVIDIA GPUs: Amazon EC2 P5e instances powered […]

Amazon Transcribe is a fully managed automatic speech recognition (ASR) service that makes it straightforward for you to add speech-to-text capabilities to your applications. Today, we are happy to announce a next-generation multi-billion parameter speech foundation model-powered system that expands automatic speech recognition to over 100 languages. In this post, we discuss some of the […]

Amazon Personalize is excited to announce the new Next Best Action (aws-next-best-action) recipe to help you determine the best actions to suggest to your individual users that will enable you to increase brand loyalty and conversion. Amazon Personalize is a fully managed machine learning (ML) service that makes it effortless for developers to deliver highly […]

Amazon SageMaker Canvas is a rich, no-code Machine Learning (ML) and Generative AI workspace that has allowed customers all over the world to more easily adopt ML technologies to solve old and new challenges thanks to its visual, no-code interface. It does so by covering the ML workflow end-to-end: whether you’re looking for powerful data […]