Category: *Post Types

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

In this post, we discuss how you can use AWS HealthScribe with Amazon Q Business to create a chatbot to quickly gain insights into patient clinician conversations.

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

In this post, we share how DPG Media is introducing AI-powered processes using Amazon Bedrock into its video publication pipelines. This solution is helping accelerate audio metadata extraction, create a more engaging user experience, and save time.

In this post, we highlight how the AWS Generative AI Innovation Center collaborated with SailPoint Technologies to build a generative AI-based coding assistant that uses Anthropic’s Claude Sonnet on Amazon Bedrock to help accelerate the development of software as a service (SaaS) connectors.

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 demonstrate how Intact’s Call Quality solution used Amazon Transcribe and other AWS services to improve critical KPIs with AI-powered contact center call auditing and analytics.

In this post, we present an Amazon Bedrock powered virtual assistant that can transcribe presentation audio and examine it for language use, grammatical errors, filler words, and repetition of words and sentences to provide recommendations as well as suggest a curated version of the speech to elevate the presentation.

In this post, we discuss Bria’s family of models, explain the Amazon 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.