Most organizations evaluating foundation models limit their analysis to three primary dimensions: accuracy, latency, and cost. While these metrics provide a useful starting point, they represent an oversimplification of the complex interplay of factors that determine real-world model performance.
Foundation models have revolutionized how enterprises...
This post is the second part of the GPT-OSS series focusing on model customization with Amazon SageMaker AI. In Part 1, we demonstrated fine-tuning GPT-OSS models using open source Hugging Face libraries with SageMaker training jobs, which supports distributed multi-GPU and multi-node configurations, so...
Amazon SageMaker Unified Studio is a single integrated development environment (IDE) that brings together your data tools for analytics and AI. As part of the next generation of Amazon SageMaker, it contains integrated tooling for building data pipelines, sharing datasets, monitoring data governance, running...
As an Amazon Web Services (AWS) enterprise customer, you’re probably exploring ways to use generative AI to enhance your business processes, improve customer experiences, and drive innovation.
With a variety of options available—from Amazon Q Business to other AWS services or third-party offerings—choosing the right...
Today, we are excited to announce the public preview of support for inline code nodes in Amazon Bedrock Flows. With this powerful new capability, you can write Python scripts directly within your workflow, alleviating the need for separate AWS Lambda functions for simple logic....
AI-powered apps and AI-powered service delivery are key differentiators in the enterprise space today. A generative AI-based resource can greatly reduce the onboarding time for new employees, enhance enterprise search, assist in drafting content, check for compliance, understand the legal language of data, and...
