Category: Learning Levels

As generative AI moves from proofs of concept (POCs) to production, we’re seeing a massive shift in how businesses and consumers interact with data, information—and each other. In what we consider “Act 1” of the generative AI story, we saw previously unimaginable amounts of data and compute create models that showcase the power of generative […]

This post is designed for Kubernetes cluster administrators and ML scientists, providing an overview of the key features that SageMaker HyperPod introduces to facilitate large-scale model training on an EKS cluster.

In this post, we aim to provide business leaders with a non-technical overview of purpose-built accelerators (PBAs) and their role within the financial services industry (FSI).

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 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 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 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.

In this post, we show you how to build an ecommerce product recommendation chatbot using Amazon Bedrock Agents and foundation models (FMs) available in Amazon Bedrock.

In this post, we delve into the process of building and deploying a sample application capable of generating multilingual descriptions for multiple images with a Streamlit UI, AWS Lambda powered with the Amazon Bedrock SDK, and AWS AppSync driven by the open source Generative AI CDK Constructs.