Category: Amazon SageMaker

Federal agencies are using cloud-based tools to increase productivity, reduce resource use, and improve their ability to efficiently serve the public. This article highlights the pioneering leaders forging new paths into cloud-based government.

United States Customs and Border Protection (CBP) is using cloud computing, generative AI and machine learning to secure US borders and address complex challenges. On a panel at the AWS Summit 2025 event in Washington, DC, two of CBP’s technology leaders discussed how the agency is combating border issues using cloud technology. In this post, we explore the key points and insights shared by the CBP technology leaders.

In this post, you will learn how AWS technologies support the entire autonomous system lifecycle from AI-powered design and virtual prototyping in pre-deployment, to edge computing through services like AWS IoT Core and AWS IoT Greengrass and related solution guidance like Tactical Edge Application Deployment on AWS and Cloud Edge Global Access (CEGA) during deployment, through real-time mission operations, and finally to post-mission evolution using secure over-the-air updates and predictive maintenance.

The post-secondary education sector is about to experience a seismic shift in how it manages data. In a landmark announcement, Amazon Web Services (AWS) and MortarCAPS have revealed a strategic partnership that promises to transform the way that tertiary education institutions handle information, support students, and drive innovation.

In this post, we walk through creating a Retrieval Augmented Generation (RAG)–powered chat assistant using Amazon SageMaker AI and Amazon Kendra to query donor data on AWS.

This post demonstrates how to enhance security and compliance in an isolated SageMaker JupyterLab environment by implementing two key customizations: configuring download restrictions and implementing secure Python package installation through AWS CodeArtifact.

Technical Safety BC (TSBC) regulates the safe installation and operation of technical systems (electrical, gas, boiler, elevator, etc.) in British Columbia. This post showcases how the TSBC built a machine learning operations (MLOps) solution using AWS to streamline production model training and management to process public safety inquiries more efficiently.

Mid-Hudson Valley Federal Credit Union (MHVFCU) needed to automate their tools and develop advanced forecasting capabilities to make informed decisions for deposit management and growth to maximize benefit for their members. For this purpose, MHVFCU leadership sought a solution that would not only save time in data preparation and analysis but also provide accurate forecasts for balances across four key deposit products: savings, checking, certificate deposits (CDs), and money market. Read this post to learn about the solution they built with AWS.

In this post, we explore how Amazon SageMaker Canvas can make machine learning accessible and actionable for nonprofit organizations. We highlight key features that allow your nonprofit to harness the power of machine learning without data science expertise or dedicated engineering teams.

Generative AI can automate tasks like code generation, testing, and documentation, but public sector customers must prioritize solutions that meet unique regulatory and operational requirements. Addressing these challenges with a secure and compliant generative AI solution can transform software development, helping organizations deliver projects on time while reducing costs. This post outlines the considerations and solutions to evaluate when selecting an AI coding solution.