Category: Artificial Intelligence

In this guest post, we explain how AWS Partner Automat-it helped their customer achieve a more than twelvefold cost savings while keeping AI model performance within the required performance thresholds. This was accomplished through careful tuning of architecture, algorithm selection, and infrastructure management.

The AWS DeepRacer League is the world’s first global autonomous racing league powered by machine learning (ML). Over the past 6 years, a diverse community of over 560,000 builders from more than 150 countries worldwide have participated in the League to learn ML fundamentals hands-on through the fun of friendly autonomous racing. After an 8-month season of nail-biting virtual qualifiers, finalists convened in person at re:Invent in Las Vegas for one final showdown to compete for prizes and glory in the high-stakes, winner-take-all AWS DeepRacer League Championship.

In this post, we demonstrate how to implement this evaluation framework using Amazon Bedrock, compare the performance of different generator models, including Anthropic’s Claude and Amazon Nova on Amazon Bedrock, and showcase how to use the new RAG evaluation feature to optimize knowledge base parameters and assess retrieval quality.

In AWS DeepRacer: How to master physical racing?, I wrote in detail about some aspects relevant to racing AWS DeepRacer in the physical world. The previous post was left open-ended—with one last Championship Final left, it was too early to share all my secrets. Now that AWS re:Invent is over, it’s time to share my strategy, how I prepared, and how it went in the end.

Pattern is a leader in ecommerce acceleration, helping brands navigate the complexities of selling on marketplaces and achieve profitable growth through a combination of proprietary technology and on-demand expertise. In this post, we share how Pattern uses AWS services to process trillions of data points to deliver actionable insights, optimizing product listings across multiple services.

In this guide, we walk you through step-by-step instructions for configuring cross-account access for Amazon Bedrock Custom Model Import, covering both non-encrypted and AWS Key Management Service (AWS KMS) based encrypted scenarios.

At ByteDance, we collaborated with Amazon Web Services (AWS) to deploy multimodal large language models (LLMs) for video understanding using AWS Inferentia2 across multiple AWS Regions around the world. By using sophisticated ML algorithms, the platform efficiently scans billions of videos each day. In this post, we discuss the use of multimodal LLMs for video understanding, the solution architecture, and techniques for performance optimization.

In 2021, Applus+ IDIADA, a global partner to the automotive industry with over 30 years of experience supporting customers in product development activities through design, engineering, testing, and homologation services, established the Digital Solutions department. In this post, we showcase the research process undertaken to develop a classifier for human interactions in this AI-based environment using Amazon Bedrock.

This post demonstrates how Amazon Bedrock Agents, combined with action groups and generative AI models, streamlines and accelerates the resolution of Terraform errors while maintaining compliance with environment security and operational guidelines.

We’re excited to announce that Mistral-Small-24B-Instruct-2501—a twenty-four billion parameter large language model (LLM) from Mistral AI that’s optimized for low latency text generation tasks—is available for customers through Amazon SageMaker JumpStart and Amazon Bedrock Marketplace. In this post, we walk through how to discover, deploy, and use Mistral-Small-24B-Instruct-2501.