Category: Artificial Intelligence

The use of large language models (LLMs) and generative AI has exploded over the last year. With the release of powerful publicly available foundation models, tools for training, fine tuning and hosting your own LLM have also become democratized. Using vLLM on AWS Trainium and Inferentia makes it possible to host LLMs for high performance […]

In this post, SophosAI shares insights in using and evaluating an out-of-the-box LLM for the enhancement of a security operations center’s (SOC) productivity using Amazon Bedrock and Amazon SageMaker. We use Anthropic’s Claude 3 Sonnet on Amazon Bedrock to illustrate the use cases.

This post walks you through Datadog’s new integration with AWS Neuron, which helps you monitor your AWS Trainium and AWS Inferentia instances by providing deep observability into resource utilization, model execution performance, latency, and real-time infrastructure health, enabling you to optimize machine learning (ML) workloads and achieve high-performance at scale.

n this post, we create a virtual analyst that can answer natural language queries of stocks matching certain technical indicator criteria using Amazon Bedrock Agents.

This post serves as a step-by-step guide on how to set up lifecycle configurations for your Amazon SageMaker Studio domains. With lifecycle configurations, system administrators can apply automated controls to their SageMaker Studio domains and their users. We cover core concepts of SageMaker Studio and provide code examples of how to apply lifecycle configuration to […]

This post presents a strategy for optimizing LLM-based applications. Given the increasing need for efficient and cost-effective AI solutions, we present a serverless read-through caching blueprint that uses repeated data patterns. With this cache, developers can effectively save and access similar prompts, thereby enhancing their systems’ efficiency and response times.

This post is co-written with Ken Kao and Hasan Ali Demirci from Rad AI. Rad AI has reshaped radiology reporting, developing solutions that streamline the most tedious and repetitive tasks, and saving radiologists’ time. Since 2018, using state-of-the-art proprietary and open source large language models (LLMs), our flagship product—Rad AI Impressions— has significantly reduced the […]

In this post, we demonstrate how to use models on Amazon Bedrock to retrieve information from images, tables, and scanned documents. We provide the following examples: 1/ performing object classification and object detection tasks, 2/ reading and querying graphs, and 3/ reading flowcharts and architecture diagrams (such as an AWS architecture diagram) and converting it to text.

Commercial Real Estate Exchange, Inc. (Crexi), is a digital marketplace and platform designed to streamline commercial real estate transactions. In this post, we will review how Crexi achieved its business needs and developed a versatile and powerful framework for AI/ML pipeline creation and deployment. This customizable and scalable solution allows its ML models to be efficiently deployed and managed to meet diverse project requirements.

We’re excited to announce the availability of Meta Llama 3.1 8B and 70B inference support on AWS Trainium and AWS Inferentia instances in Amazon SageMaker JumpStart. Trainium and Inferentia, enabled by the AWS Neuron software development kit (SDK), offer high performance and lower the cost of deploying Meta Llama 3.1 by up to 50%. In this post, we demonstrate how to deploy Meta Llama 3.1 on Trainium and Inferentia instances in SageMaker JumpStart.