Category: Amazon Machine Learning

Today’s education technology (EdTech) leaders face intense pressure to “do something with AI.” Fear of missing out has created a mentality where companies rush out AI features without understanding what problems they’re solving for their users. At AWS, we work closely with EdTech leaders navigating the generative AI landscape, and we’ve seen these patterns emerge repeatedly. Based on our experience collaborating with EdTech organizations across the education spectrum, we’ve created a list of five common pitfalls that derail generative AI efforts and the strategic approaches we recommend to avoid them.

Meeple Srl—a technology spin-off from the University of Padua and Amazon Web Services (AWS) Partner—has released the Amazon Bedrock API Provider plugin for Moodle. This open source integration enables educational institutions to seamlessly connect Moodle Learning Management Systems with Amazon Bedrock, providing access to foundation models. Read this post to learn more.

Amazon Bedrock AgentCore empowers higher education institutions to finally move beyond AI experimentation to deliver intelligent, personalized support at a campus-wide scale. Read this post to learn more.

In the rapidly evolving landscape of academic research, cloud computing and advanced digital services have revolutionized the way scientists collaborate, analyze data, and disseminate findings. AWS is partnering with higher education institutions to revolutionize how research is conducted.

What if paper forms could be processed at the same speed as digital forms? What if their contents could be automatically entered in the same database as the digital forms? Medicaid agencies could analyze data in near real-time and drive actionable insights on a single dashboard. Whether a provider submits claims electronically or on paper, the claim could be adjudicated using the same process and analyzed the same way, saving both time and money. AI and ML services from AWS allow Medicaid agencies to create this streamlined solution. Moreover, AWS enables the adoption of solutions like this by providing no code or low code serverless services. Read this post to learn more

In this post, we discuss how to use datasets in the Registry of Open Data on AWS with Amazon Bedrock Knowledge Bases. With Amazon Bedrock Knowledge Bases, you can give foundation models (FMs) and agents contextual information from private and public data sources to deliver more relevant, accurate, and customized responses.

In this post, we explore how we’re transforming educational content alignment by combining the graph database capabilities of Amazon Neptune with large language models (LLMs) accessible in Amazon Bedrock.

This post demonstrates PacBio’s whole genome sequencing variant pipeline implementation on AWS HealthOmics, offering performance optimization insights and evidence-based recommendations for cost-effective deployment at scale based on extensive benchmarking.

In this post, we explore how to build a sophisticated meeting transcription and analysis platform using AWS services, designed specifically for the scientific community. Our solution combines the power of AWS Transcribe, Amazon Bedrock, and other Amazon Web Services (AWS) services to create an intelligent tool that transforms how researchers document and analyze their discussions.

PixelGuard—built on AWS—is an advanced software solution that deidentifies medical images while preserving clinical relevance and efficacy. It uses over 75 state-of-the-art AI-driven models capable of detecting and redacting multilingual, multi-orientation text across all major formats, alongside configurable metadata anonymization. Read this post to learn more.