This blog post is co-written with George Orlin from Meta. Today, we are excited to announce that Meta’s Segment Anything Model (SAM) 2.1 vision segmentation model is publicly available through Amazon SageMaker JumpStart to deploy and run inference. Meta SAM 2.1 provides state-of-the-art video and image segmentation capabilities in aContinue Reading
Today, we are excited to announce that the Falcon 3 family of models from TII are available in Amazon SageMaker JumpStart. In this post, we explore how to deploy this model efficiently on Amazon SageMaker AI. Overview of the Falcon 3 family of models The Falcon 3 family, developed byContinue Reading
AI agents are rapidly becoming the next frontier in enterprise transformation, with 82% of organizations planning adoption within the next 3 years. According to a Capgemini survey of 1,100 executives at large enterprises, 10% of organizations already use AI agents, and more than half plan to use them in theContinue Reading
As large language models (LLMs) become increasingly integrated into customer-facing applications, organizations are exploring ways to leverage their natural language processing capabilities. Many businesses are investigating how AI can enhance customer engagement and service delivery, and facing challenges in making sure LLMs driven engagements are on topic and follow theContinue Reading
Today, we are announcing that DeepSeek AI’s first-generation frontier model, DeepSeek-R1, is available through Amazon SageMaker JumpStart and Amazon Bedrock Marketplace to deploy for inference. You can now use DeepSeek-R1 to build, experiment, and responsibly scale your generative AI ideas on AWS. In this post, we demonstrate how to getContinue Reading
Evaluating large language models (LLMs) is crucial as LLM-based systems become increasingly powerful and relevant in our society. Rigorous testing allows us to understand an LLM’s capabilities, limitations, and potential biases, and provide actionable feedback to identify and mitigate risk. Furthermore, evaluation processes are important not only for LLMs, butContinue Reading
Amazon SageMaker provides a seamless experience for building, training, and deploying machine learning (ML) models at scale. Although SageMaker offers a wide range of built-in algorithms and pre-trained models through Amazon SageMaker JumpStart, there are scenarios where you might need to bring your own custom model or use specific softwareContinue Reading
Attaching a custom Docker image to an Amazon SageMaker Studio domain involves several steps. First, you need to build and push the image to Amazon Elastic Container Registry (Amazon ECR). You also need to make sure that the Amazon SageMaker domain execution role has the necessary permissions to pull theContinue Reading
Businesses today deal with a reality that is increasingly complex and volatile. Companies across retail, manufacturing, healthcare, and other sectors face pressing challenges in accurate planning and forecasting. Predicting future inventory needs, setting achievable strategic goals, and budgeting effectively involve grappling with ever-changing consumer demand and global market forces. InventoryContinue Reading
In this post, I’ll show you how to use Amazon Bedrock—with its fully managed, on-demand API—with your Amazon SageMaker trained or fine-tuned model. Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies such as AI21 Labs, Anthropic, Cohere, Meta,Continue Reading
