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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, latency, and real-time infrastructure health, enabling you to optimize machine learning …

Enhanced observability for AWS Trainium and AWS Inferentia with Datadog Read More »

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 logic to unpack the query into parts and fetching the necessary …

Create a virtual stock technical analyst using Amazon Bedrock Agents Read More »

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 …

Apply Amazon SageMaker Studio lifecycle configurations using AWS CDK Read More »

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, the growing demand for AI-powered applications has led to a high …

Build a read-through semantic cache with Amazon OpenSearch Serverless and Amazon Bedrock Read More »

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 …

Rad AI reduces real-time inference latency by 50% using Amazon SageMaker Read More »

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 extract text, but also understand information available in images. Amazon Bedrock …

Read graphs, diagrams, tables, and scanned pages using multimodal prompts in Amazon Bedrock Read More »

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 AI progress is rapid, machine learning operations (MLOps) tooling is continuously …

How Crexi achieved ML models deployment on AWS at scale and boosted efficiency Read More »

New UK research commissioned by Google Cloud highlights the transformative potential of generative AI in the public sector.

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. Meta Llama 3.1 multilingual large language models (LLMs) are a collection of pre-trained and instruction tuned generative models. Trainium and Inferentia, enabled by the AWS Neuron software development kit …

Deploy Meta Llama 3.1 models cost-effectively in Amazon SageMaker JumpStart with AWS Inferentia and AWS Trainium Read More »