Tag: AI/ML

In this post, we share how Hapag-Lloyd developed and implemented a machine learning (ML)-powered assistant predicting vessel arrival and departure times that revolutionizes their schedule planning. By using Amazon SageMaker AI and implementing robust MLOps practices, Hapag-Lloyd has enhanced its schedule reliability—a key performance indicator in the industry and quality promise to their customers.

In this solution, we demonstrate how the user (a parent) can interact with a Strands or LangGraph agent in conversational style and get information about the immunization history and schedule of their child, inquire about the available slots, and book appointments. With some changes, AI agents can be made event-driven so that they can automatically send reminders, book appointments, and so on.

In this post, we explore how to monitor and manage Amazon Bedrock batch inference jobs using Amazon CloudWatch metrics, alarms, and dashboards to optimize performance, cost, and operational efficiency.

In this post, we showed how to use the Amazon Q Business browser extension to give your team seamless access to AI-driven insights and assistance. The browser extension is now available in US East (N. Virginia) and US West (Oregon) AWS Regions for Mozilla, Google Chrome, and Microsoft Edge as part of the Lite Subscription.

We’re excited to announce new security capabilities in Datadog Cloud Security that can help you detect and remediate Amazon Bedrock misconfigurations before they become security incidents. This integration helps organizations embed robust security controls and secure their use of the powerful capabilities of Amazon Bedrock by offering three critical advantages: holistic AI security by integrating AI security into your broader cloud security strategy, real-time risk detection through identifying potential AI-related security issues as they emerge, and simplified compliance to help meet evolving AI regulations with pre-built detections.

Amazon Q Business offers AWS customers a scalable and comprehensive solution for enhancing business processes across their organization. By carefully evaluating your use cases, following implementation best practices, and using the architectural guidance provided in this post, you can deploy Amazon Q Business to transform your enterprise productivity. The key to success lies in starting small, proving value quickly, and scaling systematically across your organization.

In this post, we walk through how you can use the new Code Editor and multiple spaces support in SageMaker Unified Studio. The sample solution shows how to develop an ML pipeline that automates the typical end-to-end ML activities to build, train, evaluate, and (optionally) deploy an ML model.

In this post, we demonstrate how to enhance AI agents’ capabilities by integrating predictive ML models using Amazon SageMaker AI and the MCP. By using the open source Strands Agents SDK and the flexible deployment options of SageMaker AI, developers can create sophisticated AI applications that combine conversational AI with powerful predictive analytics capabilities.

This post explores how Indegene’s Social Intelligence Solution uses advanced AI to help life sciences companies extract valuable insights from digital healthcare conversations. Built on AWS technology, the solution addresses the growing preference of HCPs for digital channels while overcoming the challenges of analyzing complex medical discussions on a scale.

In this post, you’ll learn how you can use Amazon Q Developer command line interface (CLI) with Model Context Protocol (MCP) servers integration to modernize a legacy Java Spring Boot application running on premises and then migrate it to Amazon Web Services (AWS) by deploying it on Amazon Elastic Kubernetes Service (Amazon EKS).