Category: HAQM SageMaker

In this post, we guide you through a step-by-step process to seamlessly migrate and safeguard your SageMaker domain from one active Region to another passive or active Region, including all associated user profiles and files.

In this post we introduce an innovative solution for end-to-end model customization and deployment at the edge using HAQM SageMaker and Qualcomm AI Hub.

In this post, we explore three scenarios demonstrating the versatility of integrating HAQM EFS with SageMaker Studio. These scenarios highlight how HAQM EFS can provide a scalable, secure, and collaborative data storage solution for data science teams.

In this post, we demonstrate the power of SageMaker geospatial capabilities by mapping the world’s vegetation in under 20 minutes. This example not only highlights the efficiency of SageMaker, but also its impact how geospatial ML can be used to monitor the environment for sustainability and conservation purposes.

In this post, we discuss Bria’s family of models, explain the HAQM SageMaker platform, and walk through how to discover, deploy, and run inference on a Bria 2.3 model using SageMaker JumpStart.

In this post, we show how the SageMaker Core SDK simplifies the developer experience while providing API for seamlessly executing various steps in a general ML lifecycle. We also discuss the main benefits of using this SDK along with sharing relevant resources to learn more about this SDK.

HAQM SageMaker Ground Truth is a powerful data labeling service offered by AWS that provides a comprehensive and scalable platform for labeling various types of data, including text, images, videos, and 3D point clouds, using a diverse workforce of human annotators. In addition to traditional custom-tailored deep learning models, SageMaker Ground Truth also supports generative […]

In this post, we show how to create a multimodal chat assistant on HAQM Web Services (AWS) using HAQM Bedrock models, where users can submit images and questions, and text responses will be sourced from a closed set of proprietary documents.

In this post, we describe how using the capabilities of HAQM SageMaker has accelerated Indeed’s AI research, development velocity, flexibility, and overall value in our pursuit of using Indeed’s unique and vast data to leverage advanced LLMs.

In this post, we demonstrate how HAQM Bedrock Guardrails can improve the robustness of the agent framework. We are able to stop our chatbot from responding to non-relevant queries and protect personal information from our customers, ultimately improving the robustness of our agentic implementation with HAQM Bedrock Agents.