AWS Machine Learning Blog
Category: HAQM SageMaker
Vision use cases with Llama 3.2 11B and 90B models from Meta
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.
Migrating to HAQM SageMaker: Karini AI Cut Costs by 23%
In this post, we share how Karini AI’s migration of vector embedding models from Kubernetes to HAQM SageMaker endpoints improved concurrency by 30% and saved over 23% in infrastructure costs.
Making traffic lights more efficient with HAQM Rekognition
In this blog post, we show you how HAQM Rekognition can mitigate congestion at traffic intersections and reduce operations and maintenance costs.
Accelerate development of ML workflows with HAQM Q Developer in HAQM SageMaker Studio
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.
Govern generative AI in the enterprise with HAQM SageMaker Canvas
In this post, we analyze strategies for governing access to HAQM 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 HAQM Bedrock models and prevent the provisioning of SageMaker endpoints with specified SageMaker JumpStart models.
Fine-tune Meta Llama 3.1 models using torchtune on HAQM SageMaker
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 HAQM SageMaker Training.
Accelerate pre-training of Mistral’s Mathstral model with highly resilient clusters on HAQM SageMaker HyperPod
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.
CRISPR-Cas9 guide RNA efficiency prediction with efficiently tuned models in HAQM SageMaker
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 […]
Improve RAG performance using Cohere Rerank
In this post, we show you how to use Cohere Rerank to improve search efficiency and accuracy in Retrieval Augmented Generation (RAG) systems.
Build ultra-low latency multimodal generative AI applications using sticky session routing in HAQM SageMaker
In this post, we explained how the new sticky routing feature in HAQM SageMaker allows you to achieve ultra-low latency and enhance your end-user experience when serving multi-modal models.