Category: Artificial Intelligence

Pre-train HyenaDNA, a transformer model exceeding 1M tokens, using HealthOmics storage and SageMaker’s managed training environment to catalyze breakthroughs in precision medicine, agriculture, and biotechnology.

Today, we are excited to announce that the first model in the next generation Falcon 2 family, the Falcon 2 11B foundation model (FM) from Technology Innovation Institute (TII), is available through Amazon SageMaker JumpStart to deploy and run inference. Falcon 2 11B is a trained dense decoder model on a 5.5 trillion token dataset […]

The General Data Protection Regulation (GDPR) right to be forgotten, also known as the right to erasure, gives individuals the right to request the deletion of their personally identifiable information (PII) data held by organizations. This means that individuals can ask companies to erase their personal data from their systems and from the systems of […]

This is a guest post co-written with CBRE. CBRE is the world’s largest commercial real estate services and investment firm, with 130,000 professionals serving clients in more than 100 countries. Services range from financing and investment to property management. CBRE is unlocking the potential of artificial intelligence (AI) to realize value across the entire commercial […]

Organizations across media and entertainment, advertising, social media, education, and other sectors require efficient solutions to extract information from videos and apply flexible evaluations based on their policies. Generative artificial intelligence (AI) has unlocked fresh opportunities for these use cases. In this post, we introduce the Media Analysis and Policy Evaluation solution, which uses AWS […]

This post is co-written with Murthy Palla and Madesh Subbanna from Vitech. Vitech is a global provider of cloud-centered benefit and investment administration software. Vitech helps group insurance, pension fund administration, and investment clients expand their offerings and capabilities, streamline their operations, and gain analytical insights. To serve their customers, Vitech maintains a repository of […]

A variety of different techniques have been used for returning images relevant to search queries. Historically, the idea of creating a joint embedding space to facilitate image captioning or text-to-image search has been of interest to machine learning (ML) practitioners and businesses for quite a while. Contrastive Language–Image Pre-training (CLIP) and Bootstrapping Language-Image Pre-training (BLIP) […]

In this post, we show you how to accelerate the full pre-training of LLM models by scaling up to 128 trn1.32xlarge nodes, using a Llama 2-7B model as an example. We share best practices for training LLMs on AWS Trainium, scaling the training on a cluster with over 100 nodes, improving efficiency of recovery from system and hardware failures, improving training stability, and achieving convergence.

Large multimodal models (LMMs) integrate multiple data types into a single model. By combining text data with images and other modalities during training, multimodal models such as Claude3, GPT-4V, and Gemini Pro Vision gain more comprehensive understanding and improved ability to process diverse data types. The multimodal approach allows models to handle a wider range […]

Mixture of Experts (MoE) architectures for large language models (LLMs) have recently gained popularity due to their ability to increase model capacity and computational efficiency compared to fully dense models. By utilizing sparse expert subnetworks that process different subsets of tokens, MoE models can effectively increase the number of parameters while requiring less computation per […]