Category: Amazon SageMaker

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 blog post, we will show you how to leverage AI21 Labs’ Task-Specific Models (TSMs) on AWS to enhance your business operations. You will learn the steps to subscribe to AI21 Labs in the AWS Marketplace, set up a domain in Amazon SageMaker, and utilize AI21 TSMs via SageMaker JumpStart.

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 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 demonstrate how to simplify data access to Amazon S3 from SageMaker Studio using S3 Access Grants, specifically for different user personas using IAM principals.

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.

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 present a real-world use case analyzing the Diabetes 130-US hospitals dataset to develop an ML model that predicts the likelihood of readmission after discharge.