Tag: technical how-to

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 how TLS inspection in Network Firewall provides visibility into encrypted traffic, even when browsers implement Encrypted Client Hello (ECH) or when SNI fields contain modified information.

Many public sector agencies consist of multiple departments, each with their own functions. This can introduce administrative delays when processing incoming requests due to challenges such as needing to manually route the ever-growing volume of paperwork to the correct destination. This blog explores how Amazon Bedrock can be used to address these challenges by classifying documents based on their key topics and appropriately distributing them. In particular, we will focus on improving the efficiency of the freedom of information (FOI) request process, but this solution can be applied to various public sector use cases.

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.

In this post, we demonstrate how to use the advanced malware detection features of Amazon GuardDuty to uncover malicious and suspicious files compromising your Amazon Elastic Compute Cloud (Amazon EC2) instances. We use the investigative capabilities of Amazon Detective to gain deeper insights into the security event. After the key questions about the security event are addressed, we outline steps to remediate the potentially compromised EC2 instance.

In this blog, we will guide you through the process of setting up a secure multi-account AWS environment using AWS Control Tower, AWS IAM Identity Center, AWS Organizations and will show you how to secure your environment using AWS Config, AWS Security Hub, and Amazon GuardDuty.

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.

This post demonstrates how to build serverless workflows on AWS that process such data using AWS Step Functions and integrate with downstream systems that have concurrency limitations.