Category: HAQM SageMaker AI

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.

Radial is the largest 3PL fulfillment provider, also offering integrated payment, fraud detection, and omnichannel solutions to mid-market and enterprise brands. In this post, we share how Radial optimized the cost and performance of their fraud detection machine learning (ML) applications by modernizing their ML workflow using HAQM SageMaker.

In this post, we demonstrate how to deploy a small language model on SageMaker AI by extending our pre-built containers to be compatible with AWS Graviton instances. We first provide an overview of the solution, and then provide detailed implementation steps to help you get started. You can find the example notebook in the GitHub repo.

In this post, we share how Impel enhances the automotive dealership customer experience with fine-tuned LLMs on SageMaker.

In this post, we explore how a leading GeoFM (Clay Foundation’s Clay foundation model available on Hugging Face) can be deployed for large-scale inference and fine-tuning on HAQM SageMaker.

In this post, we explore how HAQM Nova Canvas can solve real-world business challenges through advanced image generation techniques. We focus on two specific use cases that demonstrate the power and flexibility of this technology: interior design and product photography.

In this post, we show you how to implement and evaluate three powerful techniques for tailoring FMs to your business needs: RAG, fine-tuning, and a hybrid approach combining both methods. We provid ready-to-use code to help you experiment with these approaches and make informed decisions based on your specific use case and dataset.

In this post, we demonstrate how to retrain and quantize a model using SageMaker AI and the SiMa.ai Palette software suite. The goal is to accurately detect individuals in environments where visibility and protective equipment detection are essential for compliance and safety.

This post demonstrates how AWS LLM League’s gamified enablement accelerates partners’ practical AI development capabilities, while showcasing how fine-tuning smaller language models can deliver cost-effective, specialized solutions for specific industry needs.

In this post, we discuss how you can build an AI-powered document processing platform with open source NER and LLMs on SageMaker.