AWS Machine Learning Blog
Category: Artificial Intelligence
Getting started with Amazon Bedrock Agents custom orchestrator
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 more closely with specific business and operational needs. We outline the feature’s key benefits, including full control over orchestration, real-time adjustments, and reusability, followed by a breakdown of how it manages state transitions and contract-based interactions between Amazon Bedrock Agents and AWS Lambda.
Use Amazon Bedrock Agents for code scanning, optimization, and remediation
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 by automating the process of scanning repositories for vulnerabilities and remediating them. This post explores how you can use Amazon Bedrock to enhance the security of your repositories and maintain compliance with organizational and regulatory standards.
Create a generative AI assistant with Slack and Amazon Bedrock
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 a natural language assistant where business users can ask questions of an unstructured dataset.
Reducing hallucinations in large language models with custom intervention using Amazon Bedrock Agents
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 through different hallucination remediation techniques and offers the flexibility to detect and mitigate hallucinations using custom actions.
Deploy Meta Llama 3.1-8B on AWS Inferentia using Amazon EKS and vLLM
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 landscape of Amazon EKS. Inferentia 2 chips deliver high throughput and low latency inference, ideal for LLMs.
Serving LLMs using vLLM and Amazon EC2 instances with AWS AI chips
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 […]
Using LLMs to fortify cyber defenses: Sophos’s insight on strategies for using LLMs with Amazon Bedrock and Amazon SageMaker
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.
Enhanced observability for AWS Trainium and AWS Inferentia with 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 (ML) workloads and achieve high-performance at scale.
Create a virtual stock technical analyst using Amazon Bedrock Agents
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 configurations using AWS CDK
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 […]