Category: Amazon Machine Learning

This is the first time that the Llama models from Meta have been released with vision capabilities. These new capabilities expand the usability of Llama models from their traditional text-only applications. In this post, we demonstrate how you can use Llama 3.2 11B and 90B models for a variety of vision-based use cases.

In this post, we show you how DoorDash built a generative AI agent using Amazon Connect, Amazon Lex, and Amazon Bedrock Knowledge Bases to provide a low-latency, self-service experience for their delivery workers.

In this post, we explain how to use Anthropic Claude on Amazon Bedrock to generate synthetic data for evaluating your RAG system.

In this blog post, we show you how Amazon Rekognition can mitigate congestion at traffic intersections and reduce operations and maintenance costs.

In this post, we analyze strategies for governing access to Amazon Bedrock and SageMaker JumpStart models from within SageMaker Canvas using AWS Identity and Access Management (IAM) policies. You’ll learn how to create granular permissions to control the invocation of ready-to-use Amazon Bedrock models and prevent the provisioning of SageMaker endpoints with specified SageMaker JumpStart models.

This post offers insights for businesses aiming to use artificial intelligence (AI) and cloud technologies to enhance customer service and streamline operations. We share how Rocket Mortgage’s use of AWS services set a new industry standard and demonstrate how to apply these principles to transform your client interactions and processes.

In this post, we demonstrate how to use Amazon Bedrock Agents with a web search API to integrate dynamic web content in your generative AI application.

In this post, we walk through how AWS can help accelerate a brand’s creative efforts with access to a powerful image-to-image model from Stable Diffusion available on Amazon Bedrock to interactively create and edit art and logo images.

In this post, we present an automated solution to provide a consistent and responsible personalization experience for your customers by using smaller LLMs for website personalization tailored to businesses and industries. This decomposes the complex task into subtasks handled by task / domain adopted LLMs, adhering to company guidelines and human expertise.

In this post, we show you how Zeta Global, a data-driven marketing technology company, has built an efficient MLOps platform to streamline the end-to-end ML workflow, from data ingestion to model deployment, while optimizing resource utilization and cost efficiency.