AWS Machine Learning Blog
Category: HAQM FSx
Build RAG-based generative AI applications in AWS using HAQM FSx for NetApp ONTAP with HAQM Bedrock
In this post, we demonstrate a solution using HAQM FSx for NetApp ONTAP with HAQM Bedrock to provide a RAG experience for your generative AI applications on AWS by bringing company-specific, unstructured user file data to HAQM Bedrock in a straightforward, fast, and secure way.
Cloud-based medical imaging reconstruction using deep neural networks
Medical imaging techniques like computed tomography (CT), magnetic resonance imaging (MRI), medical x-ray imaging, ultrasound imaging, and others are commonly used by doctors for various reasons. Some examples include detecting changes in the appearance of organs, tissues, and vessels, and detecting abnormalities such as tumors and various other type of pathologies. Before doctors can use […]
Build and deploy a scalable machine learning system on Kubernetes with Kubeflow on AWS
In this post, we demonstrate Kubeflow on AWS (an AWS-specific distribution of Kubeflow) and the value it adds over open-source Kubeflow through the integration of highly optimized, cloud-native, enterprise-ready AWS services. Kubeflow is the open-source machine learning (ML) platform dedicated to making deployments of ML workflows on Kubernetes simple, portable and scalable. Kubeflow provides many […]
Securely search unstructured data on Windows file systems with the HAQM Kendra connector for HAQM FSx for Windows File Server
Critical information can be scattered across multiple data sources in your organization, including sources such as Windows file systems stored on HAQM FSx for Windows File Server. You can now use the HAQM Kendra connector for FSx for Windows File Server to index documents (HTML, PDF, MS Word, MS PowerPoint, and plain text) stored in […]