Best Practices

In this post, we discuss how to evaluate the performance of your knowledge base, including the metrics and data to use for evaluation. We also address some of the tactics and configuration changes that can improve specific metrics.

This post provides an overview of a custom solution developed by the for GoDaddy, a domain registrar, registry, web hosting, and ecommerce company that seeks to make entrepreneurship more accessible by using generative AI to provide personalized business insights to over 21 million customers. In this collaboration, the Generative AI Innovation Center team created an accurate and cost-efficient generative AI–based solution using batch inference in Amazon Bedrock, helping GoDaddy improve their existing product categorization system.

This post begins a blog series exploring DeepSeek and open FMs on Amazon Bedrock Custom Model Import. It covers the process of performance benchmarking of custom models in Amazon Bedrock using popular open source tools: LLMPerf and LiteLLM. It includes a notebook that includes step-by-step instructions to deploy a DeepSeek-R1-Distill-Llama-8B model, but the same steps apply for any other model supported by Amazon Bedrock Custom Model Import.

In this blog post, we will guide you through the process of integrating Chronos into Amazon SageMaker Pipeline using a synthetic dataset that simulates a sales forecasting scenario, unlocking accurate and efficient predictions with minimal data.

In this post, we discuss best practices for applying LLMs to generate ground truth for evaluating question-answering assistants with FMEval on an enterprise scale. FMEval is a comprehensive evaluation suite from Amazon SageMaker Clarify, and provides standardized implementations of metrics to assess quality and responsibility. To learn more about FMEval, see Evaluate large language models for quality and responsibility of LLMs.

In this two-part series, we discuss how you can reduce the DeepSeek model customization complexity by using the pre-built fine-tuning workflows (also called “recipes”) for both DeepSeek-R1 model and its distilled variations, released as part of Amazon SageMaker HyperPod recipes. In this first post, we will build a solution architecture for fine-tuning DeepSeek-R1 distilled models and demonstrate the approach by providing a step-by-step example on customizing the DeepSeek-R1 Distill Qwen 7b model using recipes, achieving an average of 25% on all the Rouge scores, with a maximum of 49% on Rouge 2 score with both SageMaker HyperPod and SageMaker training jobs. The second part of the series will focus on fine-tuning the DeepSeek-R1 671b model itself.

This guide demonstrates how to deploy an open source foundation model from Hugging Face on Amazon EC2 instances across three locations: a commercial AWS Region and two AWS Local Zones. Through comparative benchmarking tests, we illustrate how deploying foundation models in Local Zones closer to end users can significantly reduce latency—a critical factor for real-time applications such as conversational AI assistants.

This post provides detailed steps for setting up the key components of a multi-account ML platform. This includes configuring the ML Shared Services Account, which manages the central templates, model registry, and deployment pipelines; sharing the ML Admin and SageMaker Projects Portfolios from the central Service Catalog; and setting up the individual ML Development Accounts where data scientists can build and train models.

This blog post provides a comprehensive guide to implementing robust safety protections for DeepSeek-R1 and other open weight models using Amazon Bedrock Guardrails. By following this guide, you'll learn how to use the advanced capabilities of DeepSeek models while maintaining strong security controls and promoting ethical AI practices.

By leveraging the generative AI capabilities and tooling of Amazon Bedrock, you can create an intelligent nerve center that connects diverse data sources, converts data into actionable insights, and creates a comprehensive plan to mitigate supply chain risks. This post walks through how Amazon Bedrock Flows connects your business systems, monitors medical device shortages, and provides mitigation strategies based on knowledge from Amazon Bedrock Knowledge Bases or data stored in Amazon S3 directly. You’ll learn how to create a system that stays ahead of supply chain risks.

As organizations navigate the complexities of the digital realm, generative AI has emerged as a transformative force, empowering enterprises to enhance productivity, streamline workflows, and drive innovation. To maximize the value of insights generated by generative AI, it is crucial to provide simple ways for users to preserve and share these insights using commonly used tools such as email. This post explores how you can integrate Amazon Q Business with Amazon SES to email conversations to specified email addresses.

Optimizing costs of generative AI applications on AWS is critical for realizing the full potential of this transformative technology. The post outlines key cost optimization pillars, including model selection and customization, token usage, inference pricing plans, and vector database considerations.