Category: *Post Types

This post discusses how customers can maintain access to Amazon Monitron after it is closed to new customers and what some alternatives are to Amazon Monitron.

In this post, we provide a step-by-step approach of fine-tuning a Mistral model using SageMaker and import it into Amazon Bedrock using the Custom Import Model feature.

In this post, we explore some ways you can use Anthropic’s Claude 3 Sonnet’s vision capabilities to accelerate the process of moving from architecture to the prototype stage of a solution.

In this post, we share how Northpower has worked with their technology partner Sculpt to reduce the effort and carbon required to identify and remediate public safety risks. Specifically, we cover the computer vision and artificial intelligence (AI) techniques used to combine datasets into a list of prioritized tasks for field teams to investigate and mitigate.

In this post we show how you can quickly launch generative AI-enabled expert chatbots, trained on your proprietary document sets, to empower your workforce across specific aerospace roles with Amazon Q and Amazon Bedrock.

In this post, we share an ML infrastructure architecture that uses SageMaker HyperPod to support research team innovation in video generation. We will discuss the advantages and pain points addressed by SageMaker HyperPod, provide a step-by-step setup guide, and demonstrate how to run a video generation algorithm on the cluster.

In this post, we walk you through a step-by-step process to create a social media content generator app using vision, language, and embedding models (Anthropic’s Claude 3, Amazon Titan Image Generator, and Amazon Titan Multimodal Embeddings) through Amazon Bedrock API and Amazon OpenSearch Serverless.

In this post, we show how you can discover and deploy the Llama 3.2 11B Vision model using SageMaker JumpStart. We also share the supported instance types and context for all the Llama 3.2 models available in SageMaker JumpStart.

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 share how Karini AI’s migration of vector embedding models from Kubernetes to Amazon SageMaker endpoints improved concurrency by 30% and saved over 23% in infrastructure costs.