Artificial Intelligence

In this post, we review the guidance for optimizing MLOps for Sustainability on AWS, providing service-specific practices to understand and reduce the environmental impact of these workloads.

In this post, we take a closer look at Amazon Bedrock Agents. They empower you to build intelligent and context-aware generative AI applications, streamlining complex workflows and delivering natural, conversational user experiences.

In this post, we detail our collaboration in creating two proof of concept (PoC) exercises around multi-modal machine learning for survival analysis and cancer sub-typing, using genomic (gene expression, mutation and copy number variant data) and imaging (histopathology slides) data. We provide insights on interpretability, robustness, and best practices of architecting complex ML workflows on AWS with Amazon SageMaker. These multi-modal pipelines are being used on the Genomics England cancer cohort to enhance our understanding of cancer biomarkers and biology.

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.

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.

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.

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.

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.

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.

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.