AWS Machine Learning Blog

With a multitude of articles, videos, audio recordings, and other media created daily across news media companies, readers of all types—individual consumers, corporate subscribers, and more—often find it difficult to find news content that is most relevant to them. Delivering personalized news and experiences to readers can help solve this problem, and create more engaging […]

This is a guest post co-written with Tamir Rubinsky and Aviad Aranias from Nielsen Sports. Nielsen Sports shapes the world’s media and content as a global leader in audience insights, data, and analytics. Through our understanding of people and their behaviors across all channels and platforms, we empower our clients with independent and actionable intelligence […]

Amazon SageMaker Studio is a web-based, integrated development environment (IDE) for machine learning (ML) that lets you build, train, debug, deploy, and monitor your ML models. SageMaker Studio provides all the tools you need to take your models from data preparation to experimentation to production while boosting your productivity. Amazon SageMaker Canvas is a powerful […]

In this post, we show how to build a contextual text and image search engine for product recommendations using the Amazon Titan Multimodal Embeddings model, available in Amazon Bedrock, with Amazon OpenSearch Serverless.

Today, Mistral AI is bringing its latest and most capable model, Mistral Large, to Amazon Bedrock, and is committed to making future models accessible to AWS customers. Mistral AI will also use AWS AI-optimized AWS Trainium and AWS Inferentia to build and deploy its future foundation models on Amazon Bedrock, benefitting from the price, performance, scale, and security of AWS. Along with this announcement, starting today, customers can use Amazon Bedrock in the AWS Europe (Paris) Region. At launch, customers will have access to some of the latest models from Amazon, Anthropic, Cohere, and Mistral AI, expanding their options to support various use cases from text understanding to complex reasoning.

In 2023, AWS announced an expanded collaboration with Hugging Face to accelerate our customers’ generative artificial intelligence (AI) journey. Hugging Face, founded in 2016, is the premier AI platform with over 500,000 open source models and more than 100,000 datasets. Over the past year, we have partnered to make it effortless to train, fine-tune, and […]

This is a guest post co-written with Michael Feil at Gradient. Evaluating the performance of large language models (LLMs) is an important step of the pre-training and fine-tuning process before deployment. The faster and more frequent you’re able to validate performance, the higher the chances you’ll be able to improve the performance of the model. […]

Amazon SageMaker Canvas allows you to use machine learning (ML) to generate predictions without having to write any code. It does so by covering the end-to-end ML workflow: whether you’re looking for powerful data preparation and AutoML, managed endpoint deployment, simplified MLOps capabilities, or the ability to configure foundation models for generative AI, SageMaker Canvas […]

This blog post is co-written with Hwalsuk Lee at Upstage. Today, we’re excited to announce that the Solar foundation model developed by Upstage is now available for customers using Amazon SageMaker JumpStart. Solar is a large language model (LLM) 100% pre-trained with Amazon SageMaker that outperforms and uses its compact size and powerful track records […]

This is a guest post co-written with Meta’s PyTorch team and is a continuation of Part 1 of this series, where we demonstrate the performance and ease of running PyTorch 2.0 on AWS. Machine learning (ML) research has proven that large language models (LLMs) trained with significantly large datasets result in better model quality. In […]