Category: Amazon SageMaker

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.

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.

In this post, AWS collaborates with Meta’s PyTorch team to showcase how you can use PyTorch’s torchtune library to fine-tune Meta Llama-like architectures while using a fully-managed environment provided by Amazon SageMaker Training.

In this post, we present to you an in-depth guide to starting a continual pre-training job using PyTorch Fully Sharded Data Parallel (FSDP) for Mistral AI’s Mathstral model with SageMaker HyperPod.

The clustered regularly interspaced short palindromic repeat (CRISPR) technology holds the promise to revolutionize gene editing technologies, which is transformative to the way we understand and treat diseases. This technique is based in a natural mechanism found in bacteria that allows a protein coupled to a single guide RNA (gRNA) strand to locate and make […]

In this post, we show you how to use Cohere Rerank to improve search efficiency and accuracy in Retrieval Augmented Generation (RAG) systems.

In this post, we explained how the new sticky routing feature in Amazon SageMaker allows you to achieve ultra-low latency and enhance your end-user experience when serving multi-modal models.