AWS Machine Learning Blog
Category: Amazon SageMaker
Migrating to Amazon SageMaker: Karini AI Cut Costs by 23%
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.
Making traffic lights more efficient with Amazon Rekognition
In this blog post, we show you how Amazon Rekognition can mitigate congestion at traffic intersections and reduce operations and maintenance costs.
Accelerate development of ML workflows with Amazon Q Developer in Amazon 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 Amazon SageMaker Canvas
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.
Fine-tune Meta Llama 3.1 models using torchtune on Amazon 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 Amazon SageMaker Training.
Accelerate pre-training of Mistral’s Mathstral model with highly resilient clusters on Amazon 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 Amazon 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 Amazon SageMaker
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.
Build a RAG-based QnA application using Llama3 models from SageMaker JumpStart
In this post, we provide a step-by-step guide for creating an enterprise ready RAG application such as a question answering bot. We use the Llama3-8B FM for text generation and the BGE Large EN v1.5 text embedding model for generating embeddings from Amazon SageMaker JumpStart.