Category: Best Practices

In this post, we demonstrate how to use the Open AI Whisper foundation model (FM) Whisper Large V3 Turbo, available in HAQM Bedrock Marketplace, which offers access to over 140 models through a dedicated offering, to produce near real-time transcription. These transcriptions are then processed by HAQM Bedrock for summarization and redaction of sensitive information.

In this post, we demonstrate how to use large vision models (LVMs) for semantic video search using natural language and image queries. We introduce some use case-specific methods, such as temporal frame smoothing and clustering, to enhance the video search performance. Furthermore, we demonstrate the end-to-end functionality of this approach by using both asynchronous and real-time hosting options on HAQM SageMaker AI to perform video, image, and text processing using publicly available LVMs on the Hugging Face Model Hub. Finally, we use HAQM OpenSearch Serverless with its vector engine for low-latency semantic video search.

In this post, we discuss how an enterprise with multiple accounts can access a shared HAQM SageMaker HyperPod cluster for running their heterogenous workloads. We use SageMaker HyperPod task governance to enable this feature.

In this post, we show how climate tech startups are developing foundation models (FMs) that use extensive environmental datasets to tackle issues such as carbon capture, carbon-negative fuels, new materials design for microplastics destruction, and ecosystem preservation. These specialized models require advanced computational capabilities to process and analyze vast amounts of data effectively.

In this post, we give an overview of a well-established generative AI foundation, dive into its components, and present an end-to-end perspective. We look at different operating models and explore how such a foundation can operate within those boundaries. Lastly, we present a maturity model that helps enterprises assess their evolution path.

In this post, we focus on the HAQM Nova Canvas image generation model. We then provide an overview of the image generation process (diffusion) and dive deep into the input parameters for text-to-image generation with HAQM Nova Canvas.

In this post, we demonstrate how to build a custom plugin with HAQM Q Business for backend integration. This plugin can integrate existing systems, including third-party systems, with little to no development in just weeks and automate critical workflows. Additionally, we show how to safeguard the solution using HAQM Cognito and AWS IAM Identity Center, maintaining the safety and integrity of sensitive data and workflows.

This post walks you through how to create a basic hallucination detection system for RAG-based applications. We also weigh the pros and cons of different methods in terms of accuracy, precision, recall, and cost.

In this post, we show you how HAQM Q Business can help augment your generative AI needs in all the abovementioned use cases and more by answering questions, providing summaries, generating content, and securely completing tasks based on data and information in your enterprise systems.

In this post, we share comprehensive best practices and scientific insights for fine-tuning Meta Llama 3.2 multimodal models on HAQM Bedrock. By following these guidelines, you can fine-tune smaller, more cost-effective models to achieve performance that rivals or even surpasses much larger models—potentially reducing both inference costs and latency, while maintaining high accuracy for your specific use case.