AWS Machine Learning Blog
Align Meta Llama 3 to human preferences with DPO, Amazon SageMaker Studio, and Amazon SageMaker Ground Truth
In this post, we show you how to enhance the performance of Meta Llama 3 8B Instruct by fine-tuning it using direct preference optimization (DPO) on data collected with SageMaker Ground Truth.
Amazon EC2 P5e instances are generally available
In this post, we discuss the core capabilities of Amazon Elastic Compute Cloud (Amazon EC2) P5e instances and the use cases they’re well-suited for. We walk you through an example of how to get started with these instances and carry out inference deployment of Meta Llama 3.1 70B and 405B models on them.
Exploring data using AI chat at Domo with Amazon Bedrock
In this post, we share how Domo, a cloud-centered data experiences innovator is using Amazon Bedrock to provide a flexible and powerful AI solution.
How Vidmob is using generative AI to transform its creative data landscape
In this post, we illustrate how Vidmob, a creative data company, worked with the AWS Generative AI Innovation Center (GenAIIC) team to uncover meaningful insights at scale within creative data using Amazon Bedrock.
Fine-tune Llama 3 for text generation on Amazon SageMaker JumpStart
In this post, we demonstrate how to fine-tune the recently released Llama 3 models from Meta, specifically the llama-3-8b and llama-3-70b variants, using Amazon SageMaker JumpStart.
Ground truth curation and metric interpretation best practices for evaluating generative AI question answering using FMEval
In this post, we discuss best practices for working with Foundation Model Evaluations Library (FMEval) in ground truth curation and metric interpretation for evaluating question answering applications for factual knowledge and quality.
Build powerful RAG pipelines with LlamaIndex and Amazon Bedrock
In this post, we show you how to use LlamaIndex with Amazon Bedrock to build robust and sophisticated RAG pipelines that unlock the full potential of LLMs for knowledge-intensive tasks.
Evaluating prompts at scale with Prompt Management and Prompt Flows for Amazon Bedrock
In this post, we demonstrate how to implement an automated prompt evaluation system using Amazon Bedrock so you can streamline your prompt development process and improve the overall quality of your AI-generated content.
Deploy Amazon SageMaker pipelines using AWS Controllers for Kubernetes
In this post, we show how ML engineers familiar with Jupyter notebooks and SageMaker environments can efficiently work with DevOps engineers familiar with Kubernetes and related tools to design and maintain an ML pipeline with the right infrastructure for their organization. This enables DevOps engineers to manage all the steps of the ML lifecycle with the same set of tools and environment they are used to.
Effectively manage foundation models for generative AI applications with Amazon SageMaker Model Registry
In this post, we explore the new features of Model Registry that streamline foundation model (FM) management: you can now register unzipped model artifacts and pass an End User License Agreement (EULA) acceptance flag without needing users to intervene.