​[[{“value”:” 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… Read More »Serving LLMs using vLLM and Amazon EC2 instances with AWS AI chips Omri Shiv AWS Machine Learning Blog

​[[{“value”:” This post is co-written with Adarsh Kyadige and Salma Taoufiq from Sophos.  As a leader in cutting-edge cybersecurity, Sophos is dedicated to safeguarding over 500,000 organizations and millions of customers across more than 150 countries. By harnessing the power of threat intelligence, machine learning… Read More »Using LLMs to fortify cyber defenses: Sophos’s insight on strategies for using LLMs with Amazon Bedrock and Amazon SageMaker Benoît de Patoul AWS Machine Learning Blog

​[[{“value”:” This post is co-written with Curtis Maher and Anjali Thatte from Datadog.  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,… Read More »Enhanced observability for AWS Trainium and AWS Inferentia with Datadog Curtis Maher AWS Machine Learning Blog

​[[{“value”:” Stock technical analysis questions can be as unique as the individual stock analyst themselves. Queries often have multiple technical indicators like Simple Moving Average (SMA), Exponential Moving Average (EMA), Relative Strength Index (RSI), and others. Answering these varied questions would mean writing complex business… Read More »Create a virtual stock technical analyst using Amazon Bedrock Agents Bharath Sridharan AWS Machine Learning Blog

​[[{“value”:” 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… Read More »Apply Amazon SageMaker Studio lifecycle configurations using AWS CDK Gabriel Rodriguez Garcia AWS Machine Learning Blog

​[[{“value”:” In the field of generative AI, latency and cost pose significant challenges. The commonly used large language models (LLMs) often process text sequentially, predicting one token at a time in an autoregressive manner. This approach can introduce delays, resulting in less-than-ideal user experiences. Additionally,… Read More »Build a read-through semantic cache with Amazon OpenSearch Serverless and Amazon Bedrock Kamran Razi AWS Machine Learning Blog

​[[{“value”:” Anthropic has open-sourced the Model Context Protocol (MCP), a major step toward improving how AI systems connect with real-world data. By providing a universal standard, MCP simplifies the integration of AI with data sources, enabling smarter, more context-aware responses and making AI systems more… Read More »Anthropic Open Sourced Model Context Protocol (MCP): Transforming AI Integration with Universal Data Connectivity for Smarter, Context-Aware, and Scalable Applications Across Industries Sana Hassan Artificial Intelligence Category – MarkTechPost

​[[{“value”:” 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… Read More »Rad AI reduces real-time inference latency by 50% using Amazon SageMaker Ken Kao AWS Machine Learning Blog

​[[{“value”:” Large language models (LLMs) have come a long way from being able to read only text to now being able to read and understand graphs, diagrams, tables, and images. In this post, we discuss how to use LLMs from Amazon Bedrock to not only… Read More »Read graphs, diagrams, tables, and scanned pages using multimodal prompts in Amazon Bedrock Mithil Shah AWS Machine Learning Blog

​[[{“value”:” This post is co-written with Isaac Smothers and James Healy-Mirkovich from Crexi.  With the current demand for AI and machine learning (AI/ML) solutions, the processes to train and deploy models and scale inference are crucial to business success. Even though AI/ML and especially generative… Read More »How Crexi achieved ML models deployment on AWS at scale and boosted efficiency Isaac Smothers AWS Machine Learning Blog