In 2023, AWS announced an expanded collaboration with Hugging Face to accelerate our customers’ generative artificial intelligence (AI) journey. Hugging Face, founded in 2016,...

This is a guest post co-written with Michael Feil at Gradient. Evaluating the performance of large language...

Generative AI applications driven by foundational models (FMs) are enabling organizations with significant business value in customer experience, productivity, process optimization, and innovations. However,...

Amazon SageMaker multi-model endpoints (MMEs) are a fully managed capability of SageMaker inference that allows you to deploy thousands of models on a single...

With the advent of generative AI, today’s foundation models (FMs), such as the large language models (LLMs) Claude 2 and Llama 2, can perform...

Today, we’re excited to announce the availability of Llama 2 inference and fine-tuning support on AWS Trainium and AWS Inferentia instances in Amazon SageMaker...

In this post, we showcase fine-tuning a Llama 2 model using a Parameter-Efficient Fine-Tuning (PEFT) method and deploy the fine-tuned model on AWS Inferentia2....

We believe generative AI has the potential over time to transform virtually every customer experience we know. The number of companies launching generative AI...

As democratization of foundation models (FMs) becomes more prevalent and demand for AI-augmented services increases, software as a service (SaaS) providers are looking to...

As organizations deploy models to production, they are constantly looking for ways to optimize the performance of their foundation models (FMs) running on the...

Amazon SageMaker makes it straightforward to deploy machine learning (ML) models for real-time inference and offers a broad selection of ML instances spanning CPUs...

For decades, Amazon has pioneered and innovated machine learning (ML), bringing delightful experiences to its customers. From the earliest days, Amazon has used ML...