Category: Advanced (300)

When deploying a large language model (LLM), machine learning (ML) practitioners typically care about two measurements for model serving performance: latency, defined by the time it takes to generate a single token, and throughput, defined by the number of tokens generated per second. Although a single request to the deployed endpoint would exhibit a throughput […]

This post discusses how enterprises can build accurate, transparent, and secure generative AI applications while keeping full control over proprietary data. The proposed solution is a RAG pipeline using an AI-native technology stack, whose components are designed from the ground up with AI at their core, rather than having AI capabilities added as an afterthought. We demonstrate how to build an end-to-end RAG application using Cohere’s language models through Amazon Bedrock and a Weaviate vector database on AWS Marketplace.

In this post, we demonstrate how to use neural architecture search (NAS) based structural pruning to compress a fine-tuned BERT model to improve model performance and reduce inference times. Pre-trained language models (PLMs) are undergoing rapid commercial and enterprise adoption in the areas of productivity tools, customer service, search and recommendations, business process automation, and […]

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 JumpStart. Using AWS Trainium and Inferentia based instances, through SageMaker, can help users lower fine-tuning costs by up to 50%, and lower deployment costs by 4.7x, while lowering per token latency. […]

OpenAI Whisper is an advanced automatic speech recognition (ASR) model with an MIT license. ASR technology finds utility in transcription services, voice assistants, and enhancing accessibility for individuals with hearing impairments. This state-of-the-art model is trained on a vast and diverse dataset of multilingual and multitask supervised data collected from the web. Its high accuracy […]

Enterprises have access to massive amounts of data, much of which is difficult to discover because the data is unstructured. Conventional approaches to analyzing unstructured data use keyword or synonym matching. They don’t capture the full context of a document, making them less effective in dealing with unstructured data. In contrast, text embeddings use machine […]

With the rapid adoption of generative AI applications, there is a need for these applications to respond in time to reduce the perceived latency with higher throughput. Foundation models (FMs) are often pre-trained on vast corpora of data with parameters ranging in scale of millions to billions and beyond. Large language models (LLMs) are a […]

We are excited to announce the launch of Amazon DocumentDB (with MongoDB compatibility) integration with Amazon SageMaker Canvas, allowing Amazon DocumentDB customers to build and use generative AI and machine learning (ML) solutions without writing code. Amazon DocumentDB is a fully managed native JSON document database that makes it straightforward and cost-effective to operate critical […]

Amazon SageMaker Studio offers a broad set of fully managed integrated development environments (IDEs) for machine learning (ML) development, including JupyterLab, Code Editor based on Code-OSS (Visual Studio Code Open Source), and RStudio. It provides access to the most comprehensive set of tools for each step of ML development, from preparing data to building, training, […]

Text-to-image generation is a rapidly growing field of artificial intelligence with applications in a variety of areas, such as media and entertainment, gaming, ecommerce product visualization, advertising and marketing, architectural design and visualization, artistic creations, and medical imaging. Stable Diffusion is a text-to-image model that empowers you to create high-quality images within seconds. In November […]