AWS Machine Learning Blog
Getting started with Amazon Bedrock Agents custom ...
In this post, we explore how Amazon Bedrock Agents simplify the orchestration of generative AI workflows, particularly with the introduction of the custom orchestrator feature. You can use the custom orchestrator to fine-tune and optimize agentic workflows that align...
Use Amazon Bedrock Agents for code scanning, optim...
For enterprises in the realm of cloud computing and software development, providing secure code repositories is essential. As sophisticated cybersecurity threats become more prevalent, organizations must adopt proactive measures to protect their assets. Amazon Bedrock offers a powerful solution...
Create a generative AI assistant with Slack and Am...
Seamless integration of customer experience, collaboration tools, and relevant data is the foundation for delivering knowledge-based productivity gains. In this post, we show you how to integrate the popular Slack messaging service with AWS generative AI services to build...
Unleash your Salesforce data using the Amazon Q Sa...
In this post, we walk you through configuring and setting up the Amazon Q Salesforce Online connector. Thousands of companies worldwide use Salesforce to manage their sales, marketing, customer service, and other business operations. The Salesforce cloud-based platform centralizes...
Reducing hallucinations in large language models w...
This post demonstrates how to use Amazon Bedrock Agents, Amazon Knowledge Bases, and the RAGAS evaluation metrics to build a custom hallucination detector and remediate it by using human-in-the-loop. The agentic workflow can be extended to custom use cases...
Deploy Meta Llama 3.1-8B on AWS Inferentia using A...
In this post, we walk through the steps to deploy the Meta Llama 3.1-8B model on Inferentia 2 instances using Amazon EKS. This solution combines the exceptional performance and cost-effectiveness of Inferentia 2 chips with the robust and flexible...
Serving LLMs using vLLM and Amazon EC2 instances w...
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 LLMs to fortify cyber defenses: Sophos’s ins...
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...
Enhanced observability for AWS Trainium and AWS In...
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...
Create a virtual stock technical analyst using Ama...
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.
Apply Amazon SageMaker Studio lifecycle configurat...
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...
Build a read-through semantic cache with Amazon Op...
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...
Rad AI reduces real-time inference latency by 50% ...
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...
Read graphs, diagrams, tables, and scanned pages u...
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,...
How Crexi achieved ML models deployment on AWS at ...
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...
Deploy Meta Llama 3.1 models cost-effectively in A...
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...
AWS achieves ISO/IEC 42001:2023 Artificial Intelli...
Amazon Web Services (AWS) is excited to be the first major cloud service provider to announce ISO/IEC 42001 accredited certification for the following AI services: Amazon Bedrock, Amazon Q Business, Amazon Textract, and Amazon Transcribe. ISO/IEC 42001 is an...
How 123RF saved over 90% of their translation cost...
This post explores how 123RF used Amazon Bedrock, Anthropic’s Claude 3 Haiku, and a vector store to efficiently translate content metadata, significantly reduce costs, and improve their global content discovery capabilities.